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Reducing the Size of Feature Set by Using Modified MCA

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dc.contributor.author Khaing, Myo
dc.date.accessioned 2019-07-03T08:22:32Z
dc.date.available 2019-07-03T08:22:32Z
dc.date.issued 2016-02-25
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/337
dc.description.abstract Reducing the size of a feature set, without altering the original representation, is 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 (Modified-MCA) is introduced. It integrates the correlation and reliability information between each feature and each class. Moreover, the proposed method contributes the optimal p-value to improve the reliability. To evaluate the performance of proposed method, experiments are carried out on ten benchmark datasets. In the experiments, three classifiers namely AdaBoost, Decision Table, JRip are used to verify that the output feature dataset produced by proposed method outperforms. Using three different classifiers is to get more accurate average classification results than using one classifier. The proposed Modified-MCA demonstrates reducing the size of the feature subspace and promising classification results. Moreover, the results performs that the propose method is better than other well-known feature selection methods; MCA, Information Gain and Relief. en_US
dc.language.iso en en_US
dc.publisher Fourteenth International Conference On Computer Applications (ICCA 2016) en_US
dc.subject Feature Selection en_US
dc.subject Correlation en_US
dc.subject Reliability en_US
dc.subject P-value en_US
dc.subject Confidence Interval en_US
dc.title Reducing the Size of Feature Set by Using Modified MCA en_US
dc.type Article en_US


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