Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data

2019
Tariq Ali, Asif Nawaz, Hafiza Ayesha Sadia

High dimensionality is a well-known problem that has a huge number of highlights in the data, yet none is helpful for a particular data mining task undertaking, for example, classification and grouping. Therefore, selection of features is used frequently to reduce the data set dimensionality. Feature selection is a multi-target errand, which diminishes dataset dimensionality, decreases the running time, and furthermore enhances the expected precision. In the study, our goal is to diminish the quantity of features of electroencephalography data for eye state classification and achieve the same or even better classification accuracy with the least number of features. We propose a genetic algorithm-based feature selection technique with the KNN classifier. The accuracy is improved with the selected feature subset using the proposed technique as compared to the full feature set. Results prove that the classification precision of the proposed strategy is enhanced by 3 % on average when contrasted with the accuracy without feature selection.

Keywords
Classification algorithms, evolutionary computation, feature extraction, genetic algorithms.Classification algorithms, evolutionary computation, feature extraction, genetic algorithms.
DOI
10.2478/acss-2019-0015
Hyperlink
https://doi.org/10.2478/acss-2019-0015

Ali, T., Nawaz, A., Sadia, H. Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data. Applied Computer Systems, 2019, Vol. 24, No. 2, pp. 119-127. ISSN 2255-8683. e-ISSN 2255-8691. Available from: doi:10.2478/acss-2019-0015

Publication language
English (en)