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- W4289210004 abstract "AbstractProper identification of informative biomarker genes from high-dimensional datasets is an important job for easy identification of cause of any disease. Irrelevant and redundant genes may hinder the performance of the classifiers. So to enhance the performance of the classifier, feature selection is a very important phase. The main motto of the study is to enhance the diagnostic process and drug discovery. This study reports two phases of multi-filters harmony search wrapper approach. In the filter first phase, ReliefF, Fisher score, SU and CFS are applied to the original micro-array dataset to evaluate the best-ranked feature subsets individually. Then on the basis of ranking, we consider the best-ranked features (15%) from individual filter algorithms, and feature pool is prepared using first rank the features of the pool in order to reduce the variability of the ranked features and generate a more robust filter algorithm. Then the identified features are features (genes) reduced by applying the harmony search (HS) meta-heuristic algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. Six well-recognized micro-array datasets are used in this study for evaluation of the proposed method. The performance of the proposed multi-filter hybrid with wrapper harmony search SVM performs better in terms of accuracy as compared to the existing methods.KeywordsSUCFSFisher_ ScoreRelifFHSSVM" @default.
- W4289210004 created "2022-08-01" @default.
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- W4289210004 date "2022-01-01" @default.
- W4289210004 modified "2023-10-14" @default.
- W4289210004 title "Hybrid Multi-filter and Harmony Search Algorithm-Based Gene Selection Method for Cancer Classification" @default.
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- W4289210004 doi "https://doi.org/10.1007/978-981-19-2177-3_63" @default.
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