Cancer Classification from Gene Expression Data by NPPC Ensemble

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 8; no. 3; pp. 659 - 671
• Main Authors: Ghorai, S, Mukherjee, A, Sengupta, S, Dutta, P K
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Application software; Artificial Intelligence; Cancer; Cancer classification; Classification; classifier ensemble; Classifiers; combination of multiple classifiers; Computational Biology - methods; Databases, Genetic; Diagnosis; Diseases; Filters; Gene expression; Gene Expression Profiling - methods; Genetic algorithms; Genetics; Humans; microarray data analysis; Mutual information; Neoplasms - classification; Neoplasms - genetics; Neoplasms - metabolism; Oligonucleotide Array Sequence Analysis - methods; proximal classifier; Reproducibility of Results; Support vector machine classification; Support vector machines; Testing
• ISSN: 1545-5963; 1557-9964
• DOI: 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.