Use of ensemble based on GA for imbalance problem
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Otros documentos de la autoría: Cleofás Sánchez, Laura; Valdovinos Rosas, Rosa María; García, Vicente; Alejo Eleuterio, Roberto
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
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Título
Use of ensemble based on GA for imbalance problemAutoría
Fecha de publicación
2009Editor
Springer Verlag (Germany)ISSN
0302-9743Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
In real-world applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with ... [+]
In real-world applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. One method to tackle this problem consists to resample the original training set, either by over-sampling the minority class and/or under-sampling the majority class. In this paper, we propose two ensemble models (using a modular neural network and the nearest neighbor rule) trained on datasets under-sampled with genetic algorithms. Experiments with real datasets demonstrate the effectiveness of the methodology here proposed [-]
Publicado en
Lecture notes in computer science, vol. 5552 (2009), pp 547-554Derechos de acceso
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