2024-03-29T10:24:56Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/256832022-09-23T16:07:49Zcom_10234_7038com_10234_9col_10234_8634
Repositori UJI
author
Cleofás Sánchez, Laura
author
Valdovinos Rosas, Rosa María
author
García, Vicente
author
Alejo Eleuterio, Roberto
2011-07-12T10:08:21Z
2011-07-12T10:08:21Z
2009
0302-9743
http://hdl.handle.net/10234/25683
http://dx.doi.org/10.1007/978-3-642-01510-6_62
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
eng
© Springer Verlag (Germany)
Genetic algorithm
Imbalance
Nearest neighbor rule
Modular neural network
Use of ensemble based on GA for imbalance problem
info:eu-repo/semantics/article
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https://repositori.uji.es/xmlui/bitstream/10234/25683/1/33796.pdf
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