<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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...

Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data

Pubblicato online: 20 feb 2020

Pagine: 119 - 127

DOI: https://doi.org/10.2478/acss-2019-0015

Parole chiave
Classification algorithms, evolutionary computation, feature extraction, genetic algorithms

© 2019 Tariq Ali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data

Pubblicato online: 20 feb 2020

Pagine: 119 - 127

DOI: https://doi.org/10.2478/acss-2019-0015

Parole chiaveClassification algorithms, evolutionary computation, feature extraction, genetic algorithms

© 2019 Tariq Ali et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Parole chiave
Classification algorithms, evolutionary computation, feature extraction, genetic algorithms