Feature selection using mutual information based uncertainty measures for tumor classification

Feature selection is a key problem in tumor classification and related tasks. This paper presents a tumor classification approach with neighborhood rough set-based feature selection. First, some uncertainty measures such as neighborhood entropy, conditional neighborhood entropy, neighborhood mutual information and neighborhood conditional mutual information, are introduced to evaluate the relevance between genes and related decision in neighborhood rough set. Then some important properties and propositions of these measures are investigated, and the relationships among these measures are established as well. By using improved minimal-Redundancy-Maximal-Relevancy, combined with sequential forward greedy search strategy, a novel feature selection algorithm with low time complexity is proposed. Finally, several cancer classification tasks are demonstrated using the proposed approach. Experimental results show that the proposed algorithm is efficient and effective.