CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011 vol.8 Issue No.03 - May/June
Issue No.03 - May/June (2011 vol.8)
Anirban Mukherjee , Indian Institute of Technology, Kharagpur
Sanghamitra Sengupta , University of Calcutta, Kolkata
Santanu Ghorai , MCKV Institute of Engineering, Howrah and Indian Institute of Technology, Kharagpur
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.36
The most important application of microarray in gene expression analysis is to classify the unknown tissue samples according to their gene expression levels with the help of known sample expression levels. In this paper, we present a nonparallel plane proximal classifier (NPPC) ensemble that ensures high classification accuracy of test samples in a computer-aided diagnosis (CAD) framework than that of a single NPPC model. For each data set only, a few genes are selected by using a mutual information criterion. Then a genetic algorithm-based simultaneous feature and model selection scheme is used to train a number of NPPC expert models in multiple subspaces by maximizing cross-validation accuracy. The members of the ensemble are selected by the performance of the trained models on a validation set. Besides the usual majority voting method, we have introduced minimum average proximity-based decision combiner for NPPC ensemble. The effectiveness of the NPPC ensemble and the proposed new approach of combining decisions for cancer diagnosis are studied and compared with support vector machine (SVM) classifier in a similar framework. Experimental results on cancer data sets show that the NPPC ensemble offers comparable testing accuracy to that of SVM ensemble with reduced training time on average.
Cancer classification, classifier ensemble, combination of multiple classifiers, microarray data analysis, proximal classifier.
Anirban Mukherjee, Sanghamitra Sengupta, Santanu Ghorai, "Cancer Classification from Gene Expression Data by NPPC Ensemble", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 3, pp. 659-671, May/June 2011, doi:10.1109/TCBB.2010.36