Comput Math Methods Med - Feature selection in classification of eye movements using electrooculography for activity recognition.

Tópicos

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Resumo

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Resumo Limpo

activ recognit need differ requisit exampl reconnaiss system patient monitor humancomput interfac featur select play import role activ recognit data mine machin learn select subset featur effici evolutionari algorithm differenti evolut de effici optim use find inform featur eye movement use electrooculographi eog mani research use eog signal humancomput interact various comput intellig method analyz eye movement propos system involv analysi eog signal use clear base featur minimum redund maximum relev featur differenti evolut base featur work concentr featur select algorithm base de order improv classif faultless activ recognit

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