Two-Phase EA/k-NN for Feature Selection and Classification in Cancer Microarray Datasets

Abstract

Efficient and reliable methods that can find a small sample of informative genes amongst thousands are of great importance. In this area, much research is investigating the combination of advanced search strategies (to find subsets of features), and classification methods. We investigate a simple evolutionary algorithm/classifier combination on two microarray cancer datasets, where this combination is applied twice – once for feature selection, and once for further selection and classification. Our contribution are: (further) demonstration that a simple EA/classifier combination is capable of good feature discovery and classification performance with no initial dimensionality reduction; demonstration that a simple repeated EA/k-NN approach is capable of competitive or better performance than methods using more sophisticated preprocessing and classifer methods; new and challenging results on two public datasets with clear explanation of experimental setup; review material on the EA/kNN area; and specific identification of genes that our work suggests are significant regarding colon cancer and prostate cancer.