A multi-objective evolutionary approach to training set selection for support vector machine
The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Acampora, Giovanni [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea - Wang, Jiliang ELSEVIER, 2018, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:147 ; year:2018 ; day:1 ; month:05 ; pages:94-108 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.knosys.2018.02.022 |
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ELV04213076X |
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| 520 | |a The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. | ||
| 520 | |a The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. | ||
| 700 | 1 | |a Herrera, Francisco |4 oth | |
| 700 | 1 | |a Tortora, Genoveffa |4 oth | |
| 700 | 1 | |a Vitiello, Autilia |4 oth | |
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10.1016/j.knosys.2018.02.022 doi GBV00000000000150A.pica (DE-627)ELV04213076X (ELSEVIER)S0950-7051(18)30074-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 550 VZ 38.00 bkl Acampora, Giovanni verfasserin aut A multi-objective evolutionary approach to training set selection for support vector machine 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. Herrera, Francisco oth Tortora, Genoveffa oth Vitiello, Autilia oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:147 year:2018 day:1 month:05 pages:94-108 extent:15 https://doi.org/10.1016/j.knosys.2018.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 147 2018 1 0501 94-108 15 045F 004 |
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10.1016/j.knosys.2018.02.022 doi GBV00000000000150A.pica (DE-627)ELV04213076X (ELSEVIER)S0950-7051(18)30074-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 550 VZ 38.00 bkl Acampora, Giovanni verfasserin aut A multi-objective evolutionary approach to training set selection for support vector machine 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. Herrera, Francisco oth Tortora, Genoveffa oth Vitiello, Autilia oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:147 year:2018 day:1 month:05 pages:94-108 extent:15 https://doi.org/10.1016/j.knosys.2018.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 147 2018 1 0501 94-108 15 045F 004 |
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10.1016/j.knosys.2018.02.022 doi GBV00000000000150A.pica (DE-627)ELV04213076X (ELSEVIER)S0950-7051(18)30074-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 550 VZ 38.00 bkl Acampora, Giovanni verfasserin aut A multi-objective evolutionary approach to training set selection for support vector machine 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. Herrera, Francisco oth Tortora, Genoveffa oth Vitiello, Autilia oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:147 year:2018 day:1 month:05 pages:94-108 extent:15 https://doi.org/10.1016/j.knosys.2018.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 147 2018 1 0501 94-108 15 045F 004 |
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10.1016/j.knosys.2018.02.022 doi GBV00000000000150A.pica (DE-627)ELV04213076X (ELSEVIER)S0950-7051(18)30074-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 550 VZ 38.00 bkl Acampora, Giovanni verfasserin aut A multi-objective evolutionary approach to training set selection for support vector machine 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. Herrera, Francisco oth Tortora, Genoveffa oth Vitiello, Autilia oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:147 year:2018 day:1 month:05 pages:94-108 extent:15 https://doi.org/10.1016/j.knosys.2018.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 147 2018 1 0501 94-108 15 045F 004 |
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10.1016/j.knosys.2018.02.022 doi GBV00000000000150A.pica (DE-627)ELV04213076X (ELSEVIER)S0950-7051(18)30074-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 550 VZ 38.00 bkl Acampora, Giovanni verfasserin aut A multi-objective evolutionary approach to training set selection for support vector machine 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. Herrera, Francisco oth Tortora, Genoveffa oth Vitiello, Autilia oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:147 year:2018 day:1 month:05 pages:94-108 extent:15 https://doi.org/10.1016/j.knosys.2018.02.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 147 2018 1 0501 94-108 15 045F 004 |
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Enthalten in Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea Amsterdam [u.a.] volume:147 year:2018 day:1 month:05 pages:94-108 extent:15 |
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a multi-objective evolutionary approach to training set selection for support vector machine |
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A multi-objective evolutionary approach to training set selection for support vector machine |
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The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. |
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The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. |
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The Support Vector Machine (SVM) is one of the most powerful algorithms for machine learning and data mining in numerous and heterogenous application domains. However, in spite of its competitiveness, SVM suffers from scalability problems which drastically worsens its performance in terms of memory requirements and execution time. As a consequence, there is a strong emergence of approaches for supporting SVM in efficiently addressing the aforementioned problems without affecting its classification capabilities. In this scenario, methods for Training Set Selection (TSS) represent a suitable and consolidated pre-processing technique to compute a reduced but representative training dataset, and improve SVM’s scalability without deprecating its classification accuracy. Recently, TSS has been formulated as an optimization problem characterized by two objectives (the classification accuracy and the reduction rate) and solved through the application of evolutionary algorithms. However, so far, all the evolutionary approaches for TSS have been based on a so-called multi-objective a priori technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, a Pareto-based multi-objective optimization approach to the TSS problem in order to explicitly deal with both its objectives and offer a better trade-off between SVM’s classification and reduction performance. The benefits of the proposed approach are validated by a set of experiments involving well-known datasets taken from the UCI Machine Learning Database Repository. As shown by statistical tests, the application of a Pareto-based multi-objective optimization approach improves on state-of-the-art TSS techniques and enhances SVM efficiency. |
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A multi-objective evolutionary approach to training set selection for support vector machine |
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