EEG signal classification using the event-related coherence and genetic algorithm
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
Nonlinear dynamic analysis of pathological voices
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Semi-supervised learning mechanism requires new feature selection methods to work on unlabeled samples. Traditional researches deal it with the help of “filter-type” semi-feature selection mechanism, which may not work well for classification tasks. Genetic algorithm is one of widely used “wrapper-type” supervised feature selection methods. Here, we propose a novel genetic algorithm based semi-feature selection method. In essence, it uses unlabeled samples to extend the initial labeled training set with the help of classifiers, and with the feedback of classifiers, it can select more discriminative features for classification. Extensive experiments on publicly available datasets show that our proposed method outperforms both traditional supervised and state-of-the-art “filter-type” semi-feature selection algorithms