Learning Bayesian networks to perform feature selection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Using Bayesian network and AIS to perform feature subset selection
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Evaluating the performance of a Bayesian Artificial Immune System for designing fuzzy rule bases
International Journal of Hybrid Intelligent Systems
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This paper proposes the application of a novel bio-inspired algorithm as a search engine to the feature subset selection problem. We may interpret our algorithm as an Estimation of Distribution Algorithm that adopts an Artificial Immune System to implement the search process in the space of all features and a Bayesian network to implement the probabilistic model of the promising solutions. The characteristics of the proposed algorithm are the capability of effectively identifying and manipulating building blocks, maintenance of diversity in the population, and automatic control of the population size. These properties allow the algorithm to perform a multimodal search, known to be of great relevance in feature selection problems. Experiments on five datasets were carried out in order to evaluate the proposed methodology in classification problems and its performance compares favorably to that produced by contenders.