The fuzzy gene filter: an adaptive fuzzy inference system for expression array feature selection
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
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The identification of differentially expressed genes is central to microarray data analysis. Presented in this paper is an approach to differentially expressed gene identification based on a Separability Index (SI). Features are selected by identifying the optimal number of top ranking genes which result in maximum class separability. The approach was implemented on a training dataset comprising 400 samples from three types of cancers: colon, breast and lung cancer. The top 4222 genes resulted in a maximum separability of 91%. These genes were then used to classify a testing dataset comprising 250 samples, using a K-nearest neighbour (K-NN) classifier, achieving an accuracy of 92%. This outperformed a K-NN classifier trained on features selected based on p