A new data-driven method for microarray data classification

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. In this paper we propose a rule-based approach called 'Large Coverage Rule' for microarray data classification. The proposed approach is a parameter-free data-driven approach that constructs decision rule based on the expression values of a gene. A simple Rank-Based Scoring algorithm is proposed for selecting informative genes. The performance of the proposed approach is evaluated using ten publicly available gene expression data sets. From the simulation result, it is found that the proposed approach generates compact rules and produces comparatively good classification accuracy than the others. Gene ontology based biological semantics is also carried out to analyse the informative genes. Statistical analysis of test result shows that the generated rules are simple to interpret, highly comprehensible and classifies microarray data accurately.