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Effective Feature Selection for Preprocessing Step of Classification Using Modified-MCA

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dc.contributor.author Khaing, Myo
dc.contributor.author Kham, Nang Saing Moon
dc.date.accessioned 2019-11-13T02:57:08Z
dc.date.available 2019-11-13T02:57:08Z
dc.date.issued 2012-02-28
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2379
dc.description.abstract A novel metric that integrates the correlation and reliability information between each feature and each class obtained from Multiple Correspondence Analysis (MCA) is currently the popular solution to score the features for feature selection. However, it has the disadvantage that p-value which examines the reliability is conventional confidence interval. The main goal of this paper is to introduce a new classifier independent (filter-based) feature selection method, Modified Multiple Correspondence Analysis (Modified-MCA) which is designed to modify MCA, improving the reliability. The efficiency and effectiveness of proposed method is demonstrated through extensive comparisons with MCA and other feature selection methods, using five benchmark datasets provided by WEKA and UCI repository. Naïve Bayes, Decision Tree and JRip are used as the classifiers. The classification results, in terms of classification accuracy and size of feature subspace, show that the proposed ModifiedMCA outperforms three other feature selection methods, MCA, Information Gain, and Relief en_US
dc.language.iso en_US en_US
dc.publisher Tenth International Conference On Computer Applications (ICCA 2012) en_US
dc.title Effective Feature Selection for Preprocessing Step of Classification Using Modified-MCA en_US
dc.type Article en_US


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