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ICANN
2010
Springer

Using Evolutionary Multiobjective Techniques for Imbalanced Classification Data

9 years 37 min ago
Using Evolutionary Multiobjective Techniques for Imbalanced Classification Data
The aim of this paper is to study the use of Evolutionary Multiobjective Techniques to improve the performance of Neural Networks (NN). In particular, we will focus on classification problems where classes are imbalanced. We propose an evolutionary multiobjective approach where the accuracy rate of all the classes is optimized at the same time. Thus, all classes will be treated equally independently of their presence in the training data set. The chromosome of the evolutionary algorithm encodes only the weights of the training patterns missclassified by the NN, instead of all the parameters of the NN as in other approaches. Results show that the multiobjective approach is able to consider all classes at the same time, disregarding to some extent their abundance in the training set or other biases that restrain some of the classes of being learned properly. Key words: Multiobjective Machine Learning, Imbalanced data, Classification, Neural Networks, NSGA-II.
Sandra García, Ricardo Aler, Inés Ma
Added 03 Mar 2011
Updated 03 Mar 2011
Type Journal
Year 2010
Where ICANN
Authors Sandra García, Ricardo Aler, Inés María Galván
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