Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers

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Abstract

In this study, a three-phase hybrid approach is proposed for the selection and classification of high dimensional microarray data. The method uses Pearson's Correlation Coefficient (PCC) in combination with Binary Particle Swarm Optimization (BPSO) or Genetic Algorithm (GA) along with various classifiers, thereby forming a PCC-BPSO/GA-multi classifiers approach. As such, five various classifiers are employed in the final stage of the classification. It was noticed that the PCC filter showed a remarkable improvement in the classification accuracy when it was combined with BPSO or GA. This positive impact was seen to be varied for different datasets based on the final applied classifier. The performance of various combination of the hybrid technique was compared in terms of accuracy and number of selected genes. In addition to the fact that BPSO is working faster than GA, it was noticed that BPSO has better performance than GA when it is combined with PCC feature selection.

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Hameed, S. S., Muhammad, F. F., Hassan, R., & Saeed, F. (2018). Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers. Journal of Computer Science, 14(6), 868–880. https://doi.org/10.3844/jcssp.2018.868.880

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