A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data
The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sanz, Jose Antonio [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on fuzzy systems - New York, NY : Inst., 1993, 23(2015), 4, Seite 973-990 |
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| Übergeordnetes Werk: |
volume:23 ; year:2015 ; number:4 ; pages:973-990 |
| Links: |
|---|
| DOI / URN: |
10.1109/TFUZZ.2014.2336263 |
|---|
| Katalog-ID: |
OLC1959563335 |
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| 520 | |a The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. | ||
| 650 | 4 | |a learning (artificial intelligence) | |
| 650 | 4 | |a IVTURS FA RC-HD | |
| 650 | 4 | |a Measurement | |
| 650 | 4 | |a economic cycles | |
| 650 | 4 | |a Predictive models | |
| 650 | 4 | |a original FURIA | |
| 650 | 4 | |a financial data processing | |
| 650 | 4 | |a Accuracy | |
| 650 | 4 | |a generated linguistic model | |
| 650 | 4 | |a fuzzy approximative classifier | |
| 650 | 4 | |a real-world financial modeling | |
| 650 | 4 | |a fuzzy set theory | |
| 650 | 4 | |a Biological system modeling | |
| 650 | 4 | |a financial crisis | |
| 650 | 4 | |a SMOTE | |
| 650 | 4 | |a Proposals | |
| 650 | 4 | |a knowledge based systems | |
| 650 | 4 | |a type-1 interval-valued fuzzy counterparts | |
| 650 | 4 | |a real-world imbalanced financial datasets | |
| 650 | 4 | |a Data models | |
| 650 | 4 | |a learning process | |
| 650 | 4 | |a synthetic minority oversampling technique | |
| 650 | 4 | |a compact evolutionary interval-valued fuzzy rule-based classification system | |
| 650 | 4 | |a Equations | |
| 650 | 4 | |a pattern classification | |
| 650 | 4 | |a evolutionary computation | |
| 650 | 4 | |a C4.5 decision tree | |
| 650 | 4 | |a real-world financial prediction | |
| 650 | 4 | |a interval-valued fuzzy rule-based classification system with tuning and rule selection | |
| 650 | 4 | |a Datasets | |
| 650 | 4 | |a Fuzzy logic | |
| 700 | 1 | |a Bernardo, Dario |4 oth | |
| 700 | 1 | |a Herrera, Francisco |4 oth | |
| 700 | 1 | |a Bustince, Humberto |4 oth | |
| 700 | 1 | |a Hagras, Hani |4 oth | |
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10.1109/TFUZZ.2014.2336263 doi PQ20160617 (DE-627)OLC1959563335 (DE-599)GBVOLC1959563335 (PRQ)c2120-2f012cc43019ae7b5b3ae02dfb9d436811b0c81bc89a87ad7eecc6fed88278c00 (KEY)0226257620150000023000400973compactevolutionaryintervalvaluedfuzzyrulebasedcla DE-627 ger DE-627 rakwb eng 004 DNB Sanz, Jose Antonio verfasserin aut A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. learning (artificial intelligence) IVTURS FA RC-HD Measurement economic cycles Predictive models original FURIA financial data processing Accuracy generated linguistic model fuzzy approximative classifier real-world financial modeling fuzzy set theory Biological system modeling financial crisis SMOTE Proposals knowledge based systems type-1 interval-valued fuzzy counterparts real-world imbalanced financial datasets Data models learning process synthetic minority oversampling technique compact evolutionary interval-valued fuzzy rule-based classification system Equations pattern classification evolutionary computation C4.5 decision tree real-world financial prediction interval-valued fuzzy rule-based classification system with tuning and rule selection Datasets Fuzzy logic Bernardo, Dario oth Herrera, Francisco oth Bustince, Humberto oth Hagras, Hani oth Enthalten in IEEE transactions on fuzzy systems New York, NY : Inst., 1993 23(2015), 4, Seite 973-990 (DE-627)171085515 (DE-600)1149610-1 (DE-576)034198547 1063-6706 nnns volume:23 year:2015 number:4 pages:973-990 http://dx.doi.org/10.1109/TFUZZ.2014.2336263 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6849462 http://search.proquest.com/docview/1701860821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_4318 AR 23 2015 4 973-990 |
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10.1109/TFUZZ.2014.2336263 doi PQ20160617 (DE-627)OLC1959563335 (DE-599)GBVOLC1959563335 (PRQ)c2120-2f012cc43019ae7b5b3ae02dfb9d436811b0c81bc89a87ad7eecc6fed88278c00 (KEY)0226257620150000023000400973compactevolutionaryintervalvaluedfuzzyrulebasedcla DE-627 ger DE-627 rakwb eng 004 DNB Sanz, Jose Antonio verfasserin aut A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. learning (artificial intelligence) IVTURS FA RC-HD Measurement economic cycles Predictive models original FURIA financial data processing Accuracy generated linguistic model fuzzy approximative classifier real-world financial modeling fuzzy set theory Biological system modeling financial crisis SMOTE Proposals knowledge based systems type-1 interval-valued fuzzy counterparts real-world imbalanced financial datasets Data models learning process synthetic minority oversampling technique compact evolutionary interval-valued fuzzy rule-based classification system Equations pattern classification evolutionary computation C4.5 decision tree real-world financial prediction interval-valued fuzzy rule-based classification system with tuning and rule selection Datasets Fuzzy logic Bernardo, Dario oth Herrera, Francisco oth Bustince, Humberto oth Hagras, Hani oth Enthalten in IEEE transactions on fuzzy systems New York, NY : Inst., 1993 23(2015), 4, Seite 973-990 (DE-627)171085515 (DE-600)1149610-1 (DE-576)034198547 1063-6706 nnns volume:23 year:2015 number:4 pages:973-990 http://dx.doi.org/10.1109/TFUZZ.2014.2336263 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6849462 http://search.proquest.com/docview/1701860821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_4318 AR 23 2015 4 973-990 |
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10.1109/TFUZZ.2014.2336263 doi PQ20160617 (DE-627)OLC1959563335 (DE-599)GBVOLC1959563335 (PRQ)c2120-2f012cc43019ae7b5b3ae02dfb9d436811b0c81bc89a87ad7eecc6fed88278c00 (KEY)0226257620150000023000400973compactevolutionaryintervalvaluedfuzzyrulebasedcla DE-627 ger DE-627 rakwb eng 004 DNB Sanz, Jose Antonio verfasserin aut A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. learning (artificial intelligence) IVTURS FA RC-HD Measurement economic cycles Predictive models original FURIA financial data processing Accuracy generated linguistic model fuzzy approximative classifier real-world financial modeling fuzzy set theory Biological system modeling financial crisis SMOTE Proposals knowledge based systems type-1 interval-valued fuzzy counterparts real-world imbalanced financial datasets Data models learning process synthetic minority oversampling technique compact evolutionary interval-valued fuzzy rule-based classification system Equations pattern classification evolutionary computation C4.5 decision tree real-world financial prediction interval-valued fuzzy rule-based classification system with tuning and rule selection Datasets Fuzzy logic Bernardo, Dario oth Herrera, Francisco oth Bustince, Humberto oth Hagras, Hani oth Enthalten in IEEE transactions on fuzzy systems New York, NY : Inst., 1993 23(2015), 4, Seite 973-990 (DE-627)171085515 (DE-600)1149610-1 (DE-576)034198547 1063-6706 nnns volume:23 year:2015 number:4 pages:973-990 http://dx.doi.org/10.1109/TFUZZ.2014.2336263 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6849462 http://search.proquest.com/docview/1701860821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_4318 AR 23 2015 4 973-990 |
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10.1109/TFUZZ.2014.2336263 doi PQ20160617 (DE-627)OLC1959563335 (DE-599)GBVOLC1959563335 (PRQ)c2120-2f012cc43019ae7b5b3ae02dfb9d436811b0c81bc89a87ad7eecc6fed88278c00 (KEY)0226257620150000023000400973compactevolutionaryintervalvaluedfuzzyrulebasedcla DE-627 ger DE-627 rakwb eng 004 DNB Sanz, Jose Antonio verfasserin aut A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. learning (artificial intelligence) IVTURS FA RC-HD Measurement economic cycles Predictive models original FURIA financial data processing Accuracy generated linguistic model fuzzy approximative classifier real-world financial modeling fuzzy set theory Biological system modeling financial crisis SMOTE Proposals knowledge based systems type-1 interval-valued fuzzy counterparts real-world imbalanced financial datasets Data models learning process synthetic minority oversampling technique compact evolutionary interval-valued fuzzy rule-based classification system Equations pattern classification evolutionary computation C4.5 decision tree real-world financial prediction interval-valued fuzzy rule-based classification system with tuning and rule selection Datasets Fuzzy logic Bernardo, Dario oth Herrera, Francisco oth Bustince, Humberto oth Hagras, Hani oth Enthalten in IEEE transactions on fuzzy systems New York, NY : Inst., 1993 23(2015), 4, Seite 973-990 (DE-627)171085515 (DE-600)1149610-1 (DE-576)034198547 1063-6706 nnns volume:23 year:2015 number:4 pages:973-990 http://dx.doi.org/10.1109/TFUZZ.2014.2336263 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6849462 http://search.proquest.com/docview/1701860821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_4318 AR 23 2015 4 973-990 |
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10.1109/TFUZZ.2014.2336263 doi PQ20160617 (DE-627)OLC1959563335 (DE-599)GBVOLC1959563335 (PRQ)c2120-2f012cc43019ae7b5b3ae02dfb9d436811b0c81bc89a87ad7eecc6fed88278c00 (KEY)0226257620150000023000400973compactevolutionaryintervalvaluedfuzzyrulebasedcla DE-627 ger DE-627 rakwb eng 004 DNB Sanz, Jose Antonio verfasserin aut A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. learning (artificial intelligence) IVTURS FA RC-HD Measurement economic cycles Predictive models original FURIA financial data processing Accuracy generated linguistic model fuzzy approximative classifier real-world financial modeling fuzzy set theory Biological system modeling financial crisis SMOTE Proposals knowledge based systems type-1 interval-valued fuzzy counterparts real-world imbalanced financial datasets Data models learning process synthetic minority oversampling technique compact evolutionary interval-valued fuzzy rule-based classification system Equations pattern classification evolutionary computation C4.5 decision tree real-world financial prediction interval-valued fuzzy rule-based classification system with tuning and rule selection Datasets Fuzzy logic Bernardo, Dario oth Herrera, Francisco oth Bustince, Humberto oth Hagras, Hani oth Enthalten in IEEE transactions on fuzzy systems New York, NY : Inst., 1993 23(2015), 4, Seite 973-990 (DE-627)171085515 (DE-600)1149610-1 (DE-576)034198547 1063-6706 nnns volume:23 year:2015 number:4 pages:973-990 http://dx.doi.org/10.1109/TFUZZ.2014.2336263 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6849462 http://search.proquest.com/docview/1701860821 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_70 GBV_ILN_4318 AR 23 2015 4 973-990 |
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Sanz, Jose Antonio ddc 004 misc learning (artificial intelligence) misc IVTURS FA RC-HD misc Measurement misc economic cycles misc Predictive models misc original FURIA misc financial data processing misc Accuracy misc generated linguistic model misc fuzzy approximative classifier misc real-world financial modeling misc fuzzy set theory misc Biological system modeling misc financial crisis misc SMOTE misc Proposals misc knowledge based systems misc type-1 interval-valued fuzzy counterparts misc real-world imbalanced financial datasets misc Data models misc learning process misc synthetic minority oversampling technique misc compact evolutionary interval-valued fuzzy rule-based classification system misc Equations misc pattern classification misc evolutionary computation misc C4.5 decision tree misc real-world financial prediction misc interval-valued fuzzy rule-based classification system with tuning and rule selection misc Datasets misc Fuzzy logic A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data |
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004 DNB A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data learning (artificial intelligence) IVTURS FA RC-HD Measurement economic cycles Predictive models original FURIA financial data processing Accuracy generated linguistic model fuzzy approximative classifier real-world financial modeling fuzzy set theory Biological system modeling financial crisis SMOTE Proposals knowledge based systems type-1 interval-valued fuzzy counterparts real-world imbalanced financial datasets Data models learning process synthetic minority oversampling technique compact evolutionary interval-valued fuzzy rule-based classification system Equations pattern classification evolutionary computation C4.5 decision tree real-world financial prediction interval-valued fuzzy rule-based classification system with tuning and rule selection Datasets Fuzzy logic |
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A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data |
| abstract |
The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. |
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The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. |
| abstract_unstemmed |
The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results. |
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Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. 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