Abstract
Selection of genes is one of the most onerous tasks for the study of microarray data, which is accounted because of the higher number of features, rising up to tens of thousands. Feature selection is a crucial step for proper analysis and classification of microarray data. Filter methods are pre-processing algorithms that are independent of the type of classifiers used. Wrapper methods predict the advantages of adding or removing a feature from the dataset by introduction of the induction algorithm and cross validation. In our proposed technique we have tried for significant reduction of the dimensionality of the feature set namely, Leukaemia, Prostate Cancer and DLBCL datasets by passing it to various filters namely, T-test, Bhattacharyya and ReliefF. The further reduction in dimension is done in the second layer with the Mutual Information Maximisation (MIM) filter, which is further optimised by the Adaptive Genetic Algorithm (AGA).
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Begum, S., Ansari, A.A., Sultan, S., Dam, R. (2019). A Hybrid Model for Optimum Gene Selection of Microarray Datasets. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_39
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DOI: https://doi.org/10.1007/978-981-13-1280-9_39
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