Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
Feature subset selection in large dimensionality domains
Pattern Recognition
Flexible-Hybrid sequential floating search in statistical feature selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Hangman BCI: An unsupervised adaptive self-paced Brain-Computer Interface for playing games
Computers in Biology and Medicine
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In brain-computer interface (BCI) development, temporal/spectral/ spatial/statistical features can be extracted from multiple electroencephalography (EEG) signals and the number of features available could be up to thousands. Therefore, feature subset selection is an important and challenging problem in BCI design. Sequential forward floating search (SFFS) has been well recognized as one of the best feature selection methods. This paper proposes a filter-dominating hybrid SFFS method, aiming at high efficiency and insignificant accuracy sacrifice for high-dimensional feature subset selection. Experiments with this new hybrid approach have been conducted on BCI feature data, in which both linear and nonlinear classifiers as wrappers and Davies-Bouldin index and mutual information based index as filters are alternatively used to evaluate potential feature subsets. Experimental results have demonstrated the advantages and usefulness of the proposed method in high-dimensional feature subset selection for BCI design.