title: Feature selection in classification of eye movements using electrooculography for activity recognition.
abstract: 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.
authors.affiliation: Department of Computer Science and Engineering, Anna University, BIT Campus, Tiruchirappalli, Tamil Nadu 620 024, India.
authors.forename: S
authors.initials: S
authors.lastname: Mala
authors.affiliation: Department of Computer Science and Engineering, Anna University, BIT Campus, Tiruchirappalli, Tamil Nadu 620 024, India.
authors.forename: K
authors.initials: K
authors.lastname: Latha
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004585:Electrooculography / Q000379:methods*; D005123:Eye / Q000473:pathology*; D005133:Eye Movements; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012160:Retina / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014584:User-Computer Interface