Feature selection based on information theory to select effective genes for diagnosis of cancer subtypes using microarray data

Feature selection is a well-known preprocessing technique in machine learning, data mining, and especially bioinformatics microarray analysis with a high-dimension, low-sample-size (HDLSS) data. The diagnosis of genes responsible for disease using microarray data is an important issue to promoting knowledge about the mechanism of disease and improves the way of dealing with the disease. In feature selection methods based on information theory, which cover a wide range of feature selection methods, the concept of entropy is used to define criteria for relevance, redundancy, and complementarity.In this paper, we propose a new relevancy criterion based on the concept of pure continuity rather than the concept of entropy. In the proposed method, to control and reduce redundancy, the relevancy between a feature and each class is separately examined, while in most of the filter methods the value of a feature is measured based on its relation to the entire class. This solution allows us to identify the most efficient features (genes) of each class separately, while identifying common features (genes) is also possible. Discretization is another challenge in some available techniques. Using a homomorphism transformation in proposed method avoids engaging with discretization complexities, while taking advantages of it. Seven types of cancer microarrays with three types of classification models (e.g. NB, KNN, and SVM) are used to establish a comparison between the proposed method and other relevant methods. The results confirm the efficiency of the proposed method in the term of accuracy and number of selected genes as two parameters of classification.