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Modified-MCA: An Effective Feature Selection

http://hdl.handle.net/20.500.12678/0000004850
1eb9deff-ee29-49b3-8f84-dab5326b5ec9
90cc725b-9dc1-424b-b99c-ac254892a26e
Publication type
Article
Upload type
Publication
Title
Title Modified-MCA: An Effective Feature Selection
Language en
Publication date 2013-02-26
Authors
Khaing, Myo
Kham, Nang Saing Moon
Description
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.
Keywords
Correlation, Reliability, Confidence Interval, P-value, feature selection
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/748
Journal articles
Eleventh International Conference On Computer Applications (ICCA 2013)
Conference papers
Books/reports/chapters
Thesis/dissertations
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0
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