Title: Gene expression classification using binary rule majority voting genetic programming classifier
Authors: Christopher Gillies; Nilesh Patel; Jan Akervall; George Wilson
Addresses: Department of Computer Science and Engineering, Oakland University, Rochester, MI-48309, USA ' Department of Computer Science and Engineering, Oakland University, Rochester, MI-48309, USA ' Departments of Radiation Oncology and BioBank, William Beaumont Health System, 3811 W Thirteen Mile Road, Royal Oak, MI-48073, USA ' Departments of Radiation Oncology and BioBank, William Beaumont Health System, 3811 W Thirteen Mile Road, Royal Oak, MI-48073, USA
Abstract: The results of a gene expression study are difficult to interpret. To increase interpretability, researchers have developed classification techniques that produce rules to classify gene expression profiles. Genetic programming is one method to produce classification rules. These rules are difficult to interpret because they are based on complicated functions of gene expression values. We propose the binary rule majority voting genetic programming classifier (BRMVGPC) that classifies samples using binary rules based on the detection calls for genes instead of the gene expression values. BRMVGPC increases rule interpretability. We evaluate BRMVGPC on two public datasets, one brain and one prostate cancer, and achieved 88.89% and 86.39% accuracy respectively. These results are comparable to other classifiers in the gene expression profile domain. Specific contributions include a classification technique BRMVGPC and an iterative k-nearest neighbour technique for handling marginal detection call values.
Keywords: cancer classification; genetic programming; data mining; gene expression; majority voting; evolutionary computing; binary rules; majority voting; classifiers; bioinformatics; brain cancer; prostate cancer; detection calls.
International Journal of Advanced Intelligence Paradigms, 2012 Vol.4 No.3/4, pp.241 - 255
Published online: 23 Aug 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article