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Problem statement: The objective of this study is, to find the smallest set of genes that can&#13; ensure highly accurate classification of cancer from micro array data by using supervised machine&#13; learning algorithms. The significance of finding the minimum subset is three fold: The computational&#13; burden and noise arising from irrelevant genes are much reduced; the cost for cancer testing is reduced&#13; significantly as it simplifies the gene expression tests to include only a very small number of genes&#13; rather than thousands of genes; it calls for more investigation into the probable biological relationship&#13; between these small numbers of genes and cancer development and treatment. Approach: The&#13; proposed method involves two steps. In the first step, some important genes were chosen with the help&#13; of Analysis of Variance (ANOVA) ranking scheme. In the second step, the classification capability&#13; was tested for all simple combinations of those important genes using a better classifier. Results: The&#13; proposed method initially uses Support Vector Machine (SVM) classifier. Relevance Vector Machine&#13; (RVM) classifier was used for increasing the classification accuracy over SVM classifier. Conclusion:&#13; The experimental result shows that the proposed method performs the cancer classification with better&#13; accuracy when compared to the conventional methods

Cancer Classification using Support Vector Machines and Relevance Vector Machine based on Analysis of Variance Features