Using hybrid associative classifier with translation (HACT) for studying imbalanced data sets
Class imbalance may reduce the classifier performance in several recognition pattern problems. Such negative effect is more notable with least represented class (minority class) Patterns. A strategy for handling this problem consisted of treating the classes included in this problem separately (majo...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | México |
| Recursos: | Universidad Autónoma del Estado de México |
| Repositorio: | Redalyc-UAEMEX |
| OAI Identifier: | oai:redalyc.org:64323227010 |
| Acesso em linha: | https://www.redalyc.org/articulo.oa?id=64323227010 |
| Access Level: | acceso abierto |
| Palavra-chave: | Ingeniería pre Data set processing under sampling class imbalance |
| Resumo: | Class imbalance may reduce the classifier performance in several recognition pattern problems. Such negative effect is more notable with least represented class (minority class) Patterns. A strategy for handling this problem consisted of treating the classes included in this problem separately (majority and minority classes) to balance the data sets (DS). This paper has studied high sensitivity to class imbalance shown by an associative model of classification: hybrid associative classifier with translation (HACT); imbalanced DS impact on associative model performance was studied. The convenience of using sub-sampling methods for decreasing imbalanced negative effects on associative memories was analysed. This proposal¿s feasibility was based on experimental results obtained from eleven real-world datasets. |
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