Large margin classifiers to generate synthetic data for imbalanced datasets
In this paper we propose the development of an approach capable of improving the results obtained by classification algorithms when applied to imbalanced datasets. The method, called Incremental Synthetic Balancing Algorithm (ISBA), performs an iterative procedure based on large margin classifiers,...
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| Published in | Applied intelligence (Dordrecht, Netherlands) Vol. 50; no. 11; pp. 3678 - 3694 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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New York
Springer US
01.11.2020
Springer Nature B.V |
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| Abstract | In this paper we propose the development of an approach capable of improving the results obtained by classification algorithms when applied to imbalanced datasets. The method, called Incremental Synthetic Balancing Algorithm (ISBA), performs an iterative procedure based on large margin classifiers, aiming to generate synthetic samples in order to reduce the level of imbalance. In the process, we use the support vectors as the reference for the generation of new instances, allowing them to be positioned in regions with greater representativeness. Furthermore, the new samples can exceed the limits of the ones used for their generation, which enables extrapolation of the boundaries of the minority class, achieving more significant recognition of this class of interest. We present comparative experiments with other techniques, among them the SMOTE, which provide strong evidence of the applicability of the proposed approach. |
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| AbstractList | In this paper we propose the development of an approach capable of improving the results obtained by classification algorithms when applied to imbalanced datasets. The method, called Incremental Synthetic Balancing Algorithm (ISBA), performs an iterative procedure based on large margin classifiers, aiming to generate synthetic samples in order to reduce the level of imbalance. In the process, we use the support vectors as the reference for the generation of new instances, allowing them to be positioned in regions with greater representativeness. Furthermore, the new samples can exceed the limits of the ones used for their generation, which enables extrapolation of the boundaries of the minority class, achieving more significant recognition of this class of interest. We present comparative experiments with other techniques, among them the SMOTE, which provide strong evidence of the applicability of the proposed approach. |
| Author | Moraes Villela, Saulo Hasenclever Borges, Carlos Cristiano Ladeira Marques, Marcelo |
| Author_xml | – sequence: 1 givenname: Marcelo surname: Ladeira Marques fullname: Ladeira Marques, Marcelo organization: Department of Computer Science, Federal University of Juiz de Fora – sequence: 2 givenname: Saulo orcidid: 0000-0001-5958-4766 surname: Moraes Villela fullname: Moraes Villela, Saulo organization: Department of Computer Science, Federal University of Juiz de Fora – sequence: 3 givenname: Carlos Cristiano orcidid: 0000-0001-7413-2880 surname: Hasenclever Borges fullname: Hasenclever Borges, Carlos Cristiano email: cchborges@ice.ufjf.br organization: Department of Computer Science, Federal University of Juiz de Fora |
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| Keywords | Synthetic sample generation Oversampling Imbalanced learning Large margin classifiers |
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| Title | Large margin classifiers to generate synthetic data for imbalanced datasets |
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