Abstract
In this paper, we apply particle swarm optimization (PSO) to solve feature subset selection problems. The proposed PSO algorithm is combined with nearest neighbor classifiers. The algorithm is applied to classify credit risk using benchmark data sets from UCI databases. The experimental results presented in the paper demonstrate that the application of our proposed method lets to achieve better results than the existing methods in terms of solution quality and computational efficiency.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
