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This paper explores the use of rough set theory for feature selection in high dimensional haptic-based handwritten signatures (exploited for user identification). Two rough set-based methods for feature selection are analyzed, the first is a greedy approach while the second relies on genetic algorithms to find minimal subsets of attributes. Also, to further reduce the haptic feature space while maximizing...
Feature subset selection is a very vast field and it plays a vital role in the modern age because of extremely large datasets with huge number of irrelevant features. In past people have been using several approaches to find subset of features that is most relevant and appropriate. As we explore different techniques we come to know that Genetic Algorithms prove to be exceptionally good in large searches...
This study presents the comparison of attribute selection techniques which used for classifying the bad behaviors of vocational education students. There are two classification methods: hybrid classification and single classification. Hybrid classification includes two steps, step one is attribute selection by search method using genetic search and results are compared by three evaluators: 1) Correlation-based...
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