In analyzing gene expression data from micro-array, a major challenge is the definition of a feature selection criterion to judge the goodness of a subset of features with respect to a particular classification model. This paper presents a cost-sensitive approach feature selection that focuses on two fundamental requirements: (1) the quality of the features in order to promote the classifier accuracy and (2) the cost of computation due to the complexity that occurs during training and testing the classifier. The paper describes the approach in detail and includes a case study for a publicly available micro-array dataset. Results show that the proposed process yields state-of-art performance and uses only a small fraction of features that are generally used in competitive approaches on the same dataset.
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