Ensemble Feature Selection Using the Evaluation based on Distance from Average Solution (EDAS) Method

JIITA, vol.6 no.4, p.649-653, 2022, DOI: SOON

Dharyll Prince M. Abellana 1*), Robert R. Roxas 1), Demelo M. Lao 1), Paula E. Mayol 1)

1) Department of Computer Science, College of Science, University of the Philippines
Cebu, Cebu City, 6000 Cebu, Philippines

Abstract: This paper investigates the applicability of multiple criteria decision making in ensemble feature selection. This paper adopts the evaluation based on distance from average solution (EDAS) method. Results show that the proposed ensemble FS algorithm was able to reduce the dataset without compromising the performance of the classifier. The findings in this study would contribute to the literature in several ways.
For one, the paper is one of the very few works to demonstrate how MCDM can be used in feature selection. Moreover, this paper is the first to demonstrate the applicability of EDAS as an ensemble FS algorithm. As such, the findings in this paper could spark the cross-fertilization of feature selection and MCDM.

Keywords: ensemble feature selection; multiple criteria decision making; binary classification

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