Variable selection for one class classifiers. Introduction of LOVE
Year: 2025
Type: article
Abstract: BACKGROUND: Variable selection methods for regression and discrimination are well developed. However, the known methods do not adequately address the problem in the case of one class classifiers. This study aims to fill this gap. RESULTS: A new variable selection method, LOVE (Leave One Variable Excluded), created specifically for one class classifiers is proposed. LOVE belongs to the wrapper family and is interactive, in contrast to most known methods, which are automatic. The four cases considered demonstrate that the method performs well in various scenarios. SIGNIFICANCE: It is shown that LOVE can: (1) enhance classifier performance; (2) prevent overfitting and improve model stability; (3) fix outliers without deletion; and (4) provide a better understanding of what factors influence the decision.
Source: Analytica Chimica Acta
Institutions: Semenov Institute of Chemical Physics, Aalborg University
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,270
OpenAlex ID: W4411079386