Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
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 and Ranking for Data Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
OFFSS: optimal fuzzy-valued feature subset selection
IEEE Transactions on Fuzzy Systems
A heuristically perturbation of dataset to achieve a diverse ensemble of classifiers
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Unsupervised linkage learner based on local optimums
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
A heuristic diversity production approach
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
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In this paper, a new feature subset selection approach is introduced. The proposed approach consists of two phases. In the first phase, we tried to reduce the run time order of the algorithm which is critical for high dimensional datasets. In this phase, first entire dataset is classified and according to silhouette value, the best number of clusters in the dataset is found. Using this value, second, each feature is classified alone with the same cluster number and proposed entropy fuzzy measures for them are calculated. In the second phase, it is tried to find a feature subset that meets the boundaries to get a high accuracy degree. The proposed method is examined on different datasets. The examination results show that the proposed method leans to find and select the minimum number of features with negligible removing final classification accuracy, among different feature subset selection methods.