Ensemble Possibilistic K-NN for Functional Clustering of Gene Expression Profiles in Human Cancers Challenge

This paper describes the Ensemble Possibilistic K-NN algorithm for classification of gene expression profiles into three major cancer categories. In fact, a modification of forward feature selection is proposed to identify relevant feature subsets allowing for multiple possibilistic K-nearest neighbors (pK-NNs) rule experts. First, individual features are ranked according to their performance on training data and subsets of features identified using greedy approach. Each subset has significantly lower dimensionality than the original feature vector. Second, each subset is associated with pK-NN expert and the final classification decision is based on combining results produced by all experts.