An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a r...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ziyi Chen [verfasserIn] Wenbai Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 156851-156862
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:156851-156862

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3018866

Katalog-ID:	DOAJ072495138

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ072495138
003	DE-627
005	20230503150956.0
007	cr uuu---uuuuu
008	230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2020.3018866 \|2 doi
035			\|a (DE-627)DOAJ072495138
035			\|a (DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Ziyi Chen \|e verfasserin \|4 aut
245	1	3	\|a An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm
264		1	\|c 2020
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field.
650		4	\|a Machine learning
650		4	\|a forecasting problem
650		4	\|a K-means clustering method
650		4	\|a chaotic slime mould algorithm
650		4	\|a support vector regression
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Wenbai Liu \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 8(2020), Seite 156851-156862 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:8 \|g year:2020 \|g pages:156851-156862
856	4	0	\|u https://doi.org/10.1109/ACCESS.2020.3018866 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466 \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/9174730/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 8 \|j 2020 \|h 156851-156862

Indexfelder

author_variant	z c zc w l wl
matchkey_str	article:21693536:2020----::nfiinprmtrdpieuprvcorgesouigmaslseign
hierarchy_sort_str	2020
callnumber-subject-code	TK
publishDate	2020
allfields	10.1109/ACCESS.2020.3018866 doi (DE-627)DOAJ072495138 (DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466 DE-627 ger DE-627 rakwb eng TK1-9971 Ziyi Chen verfasserin aut An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field. Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression Electrical engineering. Electronics. Nuclear engineering Wenbai Liu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 156851-156862 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:156851-156862 https://doi.org/10.1109/ACCESS.2020.3018866 kostenfrei https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466 kostenfrei https://ieeexplore.ieee.org/document/9174730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 156851-156862
spelling	10.1109/ACCESS.2020.3018866 doi (DE-627)DOAJ072495138 (DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466 DE-627 ger DE-627 rakwb eng TK1-9971 Ziyi Chen verfasserin aut An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field. Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression Electrical engineering. Electronics. Nuclear engineering Wenbai Liu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 156851-156862 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:156851-156862 https://doi.org/10.1109/ACCESS.2020.3018866 kostenfrei https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466 kostenfrei https://ieeexplore.ieee.org/document/9174730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 156851-156862
allfields_unstemmed	10.1109/ACCESS.2020.3018866 doi (DE-627)DOAJ072495138 (DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466 DE-627 ger DE-627 rakwb eng TK1-9971 Ziyi Chen verfasserin aut An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field. Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression Electrical engineering. Electronics. Nuclear engineering Wenbai Liu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 156851-156862 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:156851-156862 https://doi.org/10.1109/ACCESS.2020.3018866 kostenfrei https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466 kostenfrei https://ieeexplore.ieee.org/document/9174730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 156851-156862
allfieldsGer	10.1109/ACCESS.2020.3018866 doi (DE-627)DOAJ072495138 (DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466 DE-627 ger DE-627 rakwb eng TK1-9971 Ziyi Chen verfasserin aut An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field. Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression Electrical engineering. Electronics. Nuclear engineering Wenbai Liu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 156851-156862 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:156851-156862 https://doi.org/10.1109/ACCESS.2020.3018866 kostenfrei https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466 kostenfrei https://ieeexplore.ieee.org/document/9174730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 156851-156862
allfieldsSound	10.1109/ACCESS.2020.3018866 doi (DE-627)DOAJ072495138 (DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466 DE-627 ger DE-627 rakwb eng TK1-9971 Ziyi Chen verfasserin aut An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field. Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression Electrical engineering. Electronics. Nuclear engineering Wenbai Liu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 156851-156862 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:156851-156862 https://doi.org/10.1109/ACCESS.2020.3018866 kostenfrei https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466 kostenfrei https://ieeexplore.ieee.org/document/9174730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 156851-156862
language	English
source	In IEEE Access 8(2020), Seite 156851-156862 volume:8 year:2020 pages:156851-156862
sourceStr	In IEEE Access 8(2020), Seite 156851-156862 volume:8 year:2020 pages:156851-156862
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Ziyi Chen @@aut@@ Wenbai Liu @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ072495138
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ072495138</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503150956.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2020.3018866</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ072495138</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ziyi Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">forecasting problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-means clustering method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">chaotic slime mould algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">support vector regression</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenbai Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">8(2020), Seite 156851-156862</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:156851-156862</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2020.3018866</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9174730/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2020</subfield><subfield code="h">156851-156862</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Ziyi Chen
spellingShingle	Ziyi Chen misc TK1-9971 misc Machine learning misc forecasting problem misc K-means clustering method misc chaotic slime mould algorithm misc support vector regression misc Electrical engineering. Electronics. Nuclear engineering An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm
authorStr	Ziyi Chen
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression
topic	misc TK1-9971 misc Machine learning misc forecasting problem misc K-means clustering method misc chaotic slime mould algorithm misc support vector regression misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Machine learning misc forecasting problem misc K-means clustering method misc chaotic slime mould algorithm misc support vector regression misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Machine learning misc forecasting problem misc K-means clustering method misc chaotic slime mould algorithm misc support vector regression misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm
ctrlnum	(DE-627)DOAJ072495138 (DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466
title_full	An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm
author_sort	Ziyi Chen
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2020
contenttype_str_mv	txt
container_start_page	156851
author_browse	Ziyi Chen Wenbai Liu
container_volume	8
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Ziyi Chen
doi_str_mv	10.1109/ACCESS.2020.3018866
author2-role	verfasserin
title_sort	efficient parameter adaptive support vector regression using k-means clustering and chaotic slime mould algorithm
callnumber	TK1-9971
title_auth	An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm
abstract	Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field.
abstractGer	Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field.
abstract_unstemmed	Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm
url	https://doi.org/10.1109/ACCESS.2020.3018866 https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466 https://ieeexplore.ieee.org/document/9174730/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Wenbai Liu
author2Str	Wenbai Liu
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2020.3018866
callnumber-a	TK1-9971
up_date	2024-07-04T01:17:37.797Z
_version_	1803609291147444224
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ072495138</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503150956.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2020.3018866</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ072495138</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ5bc7aa2b4ebf454298178cc3199d7466</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Ziyi Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">forecasting problem</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-means clustering method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">chaotic slime mould algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">support vector regression</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenbai Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">8(2020), Seite 156851-156862</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:156851-156862</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2020.3018866</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/5bc7aa2b4ebf454298178cc3199d7466</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9174730/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2020</subfield><subfield code="h">156851-156862</subfield></datafield></record></collection>
score	7.4017487

Nicht das Richtige dabei?

Schreiben Sie uns!

An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?