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Information Gain Measured Feature Selection to Reduce High Dimensional Data

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dc.contributor.author Win, Thee Zin
dc.contributor.author Kham, Nang Saing Moon
dc.date.accessioned 2019-07-22T07:59:08Z
dc.date.available 2019-07-22T07:59:08Z
dc.date.issued 2019-02-27
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1167
dc.description.abstract While demand of the massive amount of data to be more effective and efficient mining strategies is increasing significantly, practitioners and researchers are trying to develop scalable machine learning algorithms and strategies in turning mountains of data into nuggets. High dimension of data makes the memory, storage requirements and computational costs increased significantly. Therefore, reducing dimension can mainly improve learning performance. Feature selection, a data preprocessing technique, is effective and efficient to enhance data mining, data analytics and machine learning. Most feature selection algorithms have been trying to eliminate irrelevant features. However, removing only irrelevant features is not enough to get the best insight and patterns. Not only irrelevant features but also redundant features can degrade learning performance. Feature selection methods which can eliminate both irrelevant and redundant features are demanding in high dimensional data analytics. To solve this problem, information gain measured feature selection is presented in this work. en_US
dc.language.iso en en_US
dc.publisher Seventeenth International Conference on Computer Applications(ICCA 2019) en_US
dc.title Information Gain Measured Feature Selection to Reduce High Dimensional Data en_US
dc.type Article en_US


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