Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition
Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classif...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ekbal, Asif [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2014 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 7(2014), 4 vom: 06. Juli, Seite 597-611 |
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| Übergeordnetes Werk: |
volume:7 ; year:2014 ; number:4 ; day:06 ; month:07 ; pages:597-611 |
| Links: |
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| DOI / URN: |
10.1007/s13042-014-0268-7 |
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| Katalog-ID: |
SPR029601533 |
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| 245 | 1 | 0 | |a Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition |
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| 520 | |a Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. | ||
| 650 | 4 | |a Named entity recognition (NER) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feature selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Parameter selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiobjective optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Saha, Sriparna |4 aut | |
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10.1007/s13042-014-0268-7 doi (DE-627)SPR029601533 (SPR)s13042-014-0268-7-e DE-627 ger DE-627 rakwb eng Ekbal, Asif verfasserin aut Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. Named entity recognition (NER) (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Parameter selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multiobjective optimization (dpeaa)DE-He213 Saha, Sriparna aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 7(2014), 4 vom: 06. Juli, Seite 597-611 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:7 year:2014 number:4 day:06 month:07 pages:597-611 https://dx.doi.org/10.1007/s13042-014-0268-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2014 4 06 07 597-611 |
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10.1007/s13042-014-0268-7 doi (DE-627)SPR029601533 (SPR)s13042-014-0268-7-e DE-627 ger DE-627 rakwb eng Ekbal, Asif verfasserin aut Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. Named entity recognition (NER) (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Parameter selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multiobjective optimization (dpeaa)DE-He213 Saha, Sriparna aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 7(2014), 4 vom: 06. Juli, Seite 597-611 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:7 year:2014 number:4 day:06 month:07 pages:597-611 https://dx.doi.org/10.1007/s13042-014-0268-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2014 4 06 07 597-611 |
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10.1007/s13042-014-0268-7 doi (DE-627)SPR029601533 (SPR)s13042-014-0268-7-e DE-627 ger DE-627 rakwb eng Ekbal, Asif verfasserin aut Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. Named entity recognition (NER) (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Parameter selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multiobjective optimization (dpeaa)DE-He213 Saha, Sriparna aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 7(2014), 4 vom: 06. Juli, Seite 597-611 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:7 year:2014 number:4 day:06 month:07 pages:597-611 https://dx.doi.org/10.1007/s13042-014-0268-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2014 4 06 07 597-611 |
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10.1007/s13042-014-0268-7 doi (DE-627)SPR029601533 (SPR)s13042-014-0268-7-e DE-627 ger DE-627 rakwb eng Ekbal, Asif verfasserin aut Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. Named entity recognition (NER) (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Parameter selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multiobjective optimization (dpeaa)DE-He213 Saha, Sriparna aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 7(2014), 4 vom: 06. Juli, Seite 597-611 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:7 year:2014 number:4 day:06 month:07 pages:597-611 https://dx.doi.org/10.1007/s13042-014-0268-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2014 4 06 07 597-611 |
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10.1007/s13042-014-0268-7 doi (DE-627)SPR029601533 (SPR)s13042-014-0268-7-e DE-627 ger DE-627 rakwb eng Ekbal, Asif verfasserin aut Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. Named entity recognition (NER) (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Parameter selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multiobjective optimization (dpeaa)DE-He213 Saha, Sriparna aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 7(2014), 4 vom: 06. Juli, Seite 597-611 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:7 year:2014 number:4 day:06 month:07 pages:597-611 https://dx.doi.org/10.1007/s13042-014-0268-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2014 4 06 07 597-611 |
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Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. 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Ekbal, Asif |
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Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition Named entity recognition (NER) (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Parameter selection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Multiobjective optimization (dpeaa)DE-He213 |
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simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition |
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Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition |
| abstract |
Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. © Springer-Verlag Berlin Heidelberg 2014 |
| abstractGer |
Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. © Springer-Verlag Berlin Heidelberg 2014 |
| abstract_unstemmed |
Abstract In this paper, we propose an efficient algorithm based on the concept of multiobjective optimization (MOO) for performing feature selection and parameter optimization of any machine learning technique. Feature and parameter combinations have significant effect to the accuracy of the classifier. We perform feature selection and parameter optimization for four different classifiers, namely conditional random field, support vector machine, memory based learner and maximum entropy. The proposed algorithms are evaluated for solving the problems of named entity recognition, an important component in many text processing applications. Currently we experiment with four different languages, namely Bengali, Hindi, Telugu and English. At first the proposed MOO based technique is used to determine the appropriate features and parameters. For each of the classifiers, the algorithm produces a set of solutions on the final Pareto optimal front. Each solution represents a classifier with a particular feature and parameter combination. All these solutions are thereafter combined using a MOO based classifier ensemble technique. Evaluation results show that the proposed approach attains the F-measure (harmonic mean of recall and precision) values of 90.48, 90.44, 78.71 and 88.68 % for Bengali, Hindi, Telugu and English, respectively. We also show that for all the experimental settings the proposed feature and parameter optimization technique performs reasonably better than the baseline systems, developed with random feature subsets. Comparisons with the existing works also show the efficacy of our proposed algorithm. © Springer-Verlag Berlin Heidelberg 2014 |
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Simultaneous feature and parameter selection using multiobjective optimization: application to named entity recognition |
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https://dx.doi.org/10.1007/s13042-014-0268-7 |
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Saha, Sriparna |
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| score |
7.402872 |