Immune multiobjective optimization algorithm for unsupervised feature selection

Authors:
Xiangrong Zhang;Bin Lu;Shuiping Gou;Licheng Jiao
Affiliations:
National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi’an, China;National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi’an, China;National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi’an, China;National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi’an, China
Venue:
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Year:
2006

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Abstract

A feature selection method for unsupervised learning is proposed. Unsupervised feature selection is considered as a combination optimization problem to search for the suitable feature subset and the pertinent number of clusters by optimizing the efficient evaluation criterion for clustering and the number of features selected. Instead of combining these measures into one objective function, we make use of the multiobjective immune clonal algorithm with forgetting strategy to find the more discriminant features for clustering and the most pertinent number of clusters. The results of experiments on synthetic data and real datasets from UCI database show the effectiveness and potential of the method.