Maqola

Adaptive, Hybrid Feature Selection (AHFS)

Zsolt János ViharosInstitute for Computer Science and Control (SZTAKI), Centre of Excellence in Production Informatics and Control, Eötvös Loránd Research Network (ELKH), Research Laboratory on Engineering and Management Intelligence, Intelligent Processes Research Group, H-1111, Budapest, Hungary, Kende u. 13–17., HungaryKrisztián Balázs KisInstitute for Computer Science and Control (SZTAKI), Centre of Excellence in Production Informatics and Control, Eötvös Loránd Research Network (ELKH), Research Laboratory on Engineering and Management Intelligence, Intelligent Processes Research Group, H-1111, Budapest, Hungary, Kende u. 13–17., HungaryÁdám FodorEötvös Loránd University, Department of Software Technology and Methodology, Budapest, H-1117, Pázmány P. sny 1/C., HungaryMáté István BükiInstitute for Computer Science and Control (SZTAKI), Centre of Excellence in Production Informatics and Control, Eötvös Loránd Research Network (ELKH), Research Laboratory on Engineering and Management Intelligence, Intelligent Processes Research Group, H-1111, Budapest, Hungary, Kende u. 13–17., Hungary

2021en

ABI

Annotatsiya

This paper deals with the problem of integrating the most suitable feature selection methods for a given problem in order to achieve the best feature order. A new, adaptive and hybrid feature selection approach is proposed, which combines and utilizes multiple individual methods in order to achieve a more generalized solution. Various state-of-the-art feature selection methods are presented in detail with examples of their applications and an exhaustive evaluation is conducted to measure and compare the their performance with the proposed approach. Results prove that while the individual feature selection methods may perform with high variety on the test cases, the combined algorithm steadily provides noticeably better solution.

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Identifikatorlar

DOI: 10.1016/j.patcog.2021.107932

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