Ensemble classifier design selecting important genes based on extracted features
by Soumen Kumar Pati; Asit Kumar Das
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 19, No. 2, 2017

Abstract: Ensemble classifier highly depends on the nature of the dataset and efficiency of the classifier degrades tremendously due to presence of irrelevant features. Because of the distinct characteristics inherent to specific cancer, selecting the most informative genes from high volume microarray dataset is challenging bioinformatics research topic. In the paper, the informative genes are selected based on some prominent features generated using statistical and probabilistic concepts. The selected genes are applied on genetic algorithm which intelligently selects an appropriate combination of classifiers where non-linear uniform cellular automata are employed to generate the initial population, multipoint-crossover and unique jumping gene mechanism for mutation to preserve the diversity in the population and a steady state fitness function is introduced for maximum accuracy with minimum classifiers where many classifiers of distinct characteristics are considered as base classifiers. Performance of the proposed method is compared with the state-of-art algorithms to demonstrate its effectiveness.

Online publication date: Thu, 11-Jan-2018

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