An Ensemble Based Approach for Feature Selection

Abstract : This paper proposes an ensemble based approach for feature selection. We aim at overcoming the problem of parameter sensitivity of feature selection approaches. To do this we employ ensemble method. We get the results per different possible threshold values automatically in our algorithm. For each threshold value, we get a subset of features. We give a score to each feature in these subsets. Finally by use of ensemble method, we select the features which have the highest scores. This method is not a parameter sensitive one, and also it has been shown that using the method based on the fuzzy entropy results in more reliable selected features than the previous methods’. Empirical results show that although the efficacy of the method is not considerably decreased in most of cases, the method becomes free from setting of any parameter.
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Communication dans un congrès
Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.240-246, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_27〉
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Behrouz Minaei-Bidgoli, Maryam Asadi, Hamid Parvin. An Ensemble Based Approach for Feature Selection. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.240-246, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_27〉. 〈hal-01571365〉

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