Multicategory cancer classification from gene expression data by multiclass NPPC ensemble

The discovery of DNA microarray technologies have given immense opportunity to make gene expression profiles for different cancer types. Besides binary classification such as normal versus tumor samples the discrimination of multiple tumor types is also important. In this work, we have first extended the recently developed binary nonparallel plane proximal classifier (NPPC) to multiclass NPPC by decomposition techniques. The multiclass NPPC is then used in a computer aided diagnosis framework to classify multicategory cancer from gene expression data by selecting very few genes by using mutual information criterion. The idea of binary NPPC ensemble is extended to form multiclass NPPC ensemble. Besides usual majority voting method, we have introduced minimum average proximity based decision combiner for multiclass NPPC ensemble. The effectiveness of the proposed method are demonstrated on four benchmark microarray data sets and compared with support vector machine (SVM) classifier in a similar framework.