Adil, B.-H. and Youssef, G. and Abderrahim, E.Q. (2017) HVS-MRMR wrapper method for variables selection. In: UNSPECIFIED.

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This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). Our algorithm based on wrapper approach using multi-layer perceptron. We call this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables. We evaluate the performance of HVS-MRMR on four benchmark classification problems. The experimental results show that HVS-MRMR selects a less number of variables with high classification accuracy compared to MRMR, HVS. HVS-MRMR can be applied to various classification problems requiring high classification accuracy. © 2017 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Benchmarking; Classification (of information); Intelligent systems; Multilayer neural networks; Redundancy, Benchmark classification; Classification accuracy; Convex combinations; Minimum redundancy-maximum relevances; Multi layer perceptron; Variable selection; Variable selection algorithms; Variables selections, Computer vision
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46

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