In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact....

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...

Feature selection involves the process of identifying the most useful feature's subset which produces compatible results similar to original set of feature. Efficiency and effectiveness are the two measures to evaluate feature selection algorithm. The time to find the cluster concerns to efficiency, while effectiveness is concerned to quality of subset feature. With these criteria, fast cl...

Feature selection involves the process of identifying the most useful feature's subset which produces compatible results similar to original set of feature. Efficiency and effectiveness are the two measures to evaluate feature selection algorithm. The time to find the cluster concerns to efficiency, while effectiveness is concerned to quality of subset feature. With these criteria, fast cl...

Feature selection involves the process of identifying the most useful feature's subset which produces compatible results similar to original set of feature. Efficiency and effectiveness are the two measures to evaluate feature selection algorithm. The time to find the cluster concerns to efficiency, while effectiveness is concerned to quality of subset feature. With these criteria, fast cl...

Feature selection involves the process of identifying the most useful feature's subset which produces compatible results similar to original set of feature. Efficiency and effectiveness are the two measures to evaluate feature selection algorithm. The time to find the cluster concerns to efficiency, while effectiveness is concerned to quality of subset feature. With these criteria, fast cl...

High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough se...

Feature selection algorithm plays a crucial role in intrusion detection, data mining and pattern recognition. According to some evaluation criteria, it gets optimal feature subset by deleting unrelated and redundant features of the original data set. Aiming at solving the problems about the low accuracy, the high false positive rate and the long detection time of the existing feature selection ...