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Modified-MCA: An Effective Feature Selection

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dc.contributor.author Khaing, Myo
dc.contributor.author Kham, Nang Saing Moon
dc.date.accessioned 2019-07-11T07:15:52Z
dc.date.available 2019-07-11T07:15:52Z
dc.date.issued 2013-02-26
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/748
dc.description.abstract Feature selection is often an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information improves the performance of machine learning algorithms. In this paper, Modified-Multiple Correspondence Analysis (M-MCA) is proposed. It explores the correlation between different features and classes to score the features for feature selection. To evaluate the performance of proposed Modified-MCA, experiments are carried out on ten benchmark datasets. In the experiments, AdaBoost, Decision Table, JRip, Naïve Bayes, and Sequential Minimal Optimization (SMO) are used as the classifiers. The proposed Modified-MCA demonstrates the promising classification results and performs better than other well-known feature selection methods; Information Gain and Relief. en_US
dc.language.iso en en_US
dc.publisher Eleventh International Conference On Computer Applications (ICCA 2013) en_US
dc.subject Correlation en_US
dc.subject Reliability en_US
dc.subject Confidence Interval en_US
dc.subject P-value en_US
dc.subject feature selection en_US
dc.title Modified-MCA: An Effective Feature Selection en_US
dc.type Article en_US


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