A novel fitness function in genetic algorithms to optimize neural networks for imbalanced data sets

Kuan Chieh Huang, Yau Hwang Kuo, I. Cheng Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The imbalanced data sets are often encountered in business, industry and real life applications. In this paper, the novel fitness function in genetic algorithms to optimize neural networks is proposed for solving the classification problems in imbalanced data sets. Not only the parameters of neural networks but also the links-pruning between neurons are regarded as an optimization problem in this study. The fitness function consists of the mean square error, the classification error rate for each class, the distances between the examples and the boundary of classification. The artificial data set and the UCI data sets are used to verify the classifier we proposed. The experimental results showed that the classifier performs better than the conventional back-propagation neural network.

Original languageEnglish
Title of host publicationProceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
Pages647-650
Number of pages4
DOIs
Publication statusPublished - 2008
Event8th International Conference on Intelligent Systems Design and Applications, ISDA 2008 - Kaohsiung, Taiwan
Duration: 2008 Nov 262008 Nov 28

Publication series

NameProceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
Volume2

Other

Other8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
CountryTaiwan
CityKaohsiung
Period08-11-2608-11-28

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

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