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A Combined Feature Selection Technique for Improving Classification Accuracy
Abstract
Feature selection has become revenue to many research regions that manage machine learning and data mining since it allows the classifiers to be cost-efficient, time-saving, and more precise. In this chapter, the feature selection strategy is consolidating by utilizing the combined feature selection technique, specifically recursive feature elimination, chi-square, info-gain, and principal component analysis. Machine learning algorithms like logistic regression, random support vector machine, and decision trees are applied in three different datasets that are pre-processed with combined feature selection technique. Then these algorithms are ensembled using voting classifier. The improvement in accuracy of the classifiers is observed by the impact of the combined feature selection.
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