A Method for Attribute Selection in Inductive Learning Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-fuzzy feature evaluation with theoretical analysis
Neural Networks
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Feature subset selection using a new definition of classifiability
Pattern Recognition Letters
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
OFFSS: optimal fuzzy-valued feature subset selection
IEEE Transactions on Fuzzy Systems
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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.