Abstract
Feature Selection can be done in most of the medical domains to identify the most suitable features that result in the accuracy of classification and to reduce time of computation; as it works on reduced number of features. The nature of the problem domain and the design issues of soft computing methods used determines the effectiveness of feature selection methods. The study includes the feature selection using Genetic Algorithm (GA), to generate the best feature subset of WBCD breast cancer dataset. The features with the best fitness value are selected for classification. Classification is done using a guided approach called Support Vector Machine (SVM) along with some constraints to specify the performance measures of classification.
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Kompalli, V.S., Kuruba, U.R. (2017). Combined Effect of Soft Computing Methods in Classification. In: Satapathy, S., Prasad, V., Rani, B., Udgata, S., Raju, K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore. https://doi.org/10.1007/978-981-10-2471-9_49
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DOI: https://doi.org/10.1007/978-981-10-2471-9_49
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