Microarray Gene Expression and Multiclass Cancer Classification Using Improved PSO Based Evolutionary Fuzzy ELM Classifier with ICGA Gene Selection

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Cancer has become one of the dreadful and most widely spreading diseases in recent years. Cancer diagnosis has become an active research area in the field of medical image processing. DNA microarrays are considered as an effectual tool used in molecular biology and cancer diagnosis. As the superiority of this method has been recognized, a variety of open queries occur about appropriate test of microarray data. As the numbers of cancer victims are increasing tremendously, the requirement of an efficient and accurate cancer classification system has become essential. For the above impenetrability and to obtain better consequences of the system with accuracy a combination of Integer-Coded Genetic Algorithm (ICGA) and Improved Particle Swarm Optimization (IPSO), coupled with an evolutionary fuzzy extreme learning machine (E-FELM), is used for gene selection and cancer classification. ICGA is used with IPSO based E-FELM classifier to chose an optimal set of genes which results in an efficient hybrid algorithm that can handle sparse data and sample imbalance. The performance of the proposed approach is evaluated and the results are compared with existing methods
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Biology and Genetics; Classifier Design and Evaluation; Feature Evaluation and Selection

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