Research Article Ensemble Feature Selection in Binary Machine Learning Classification: A Novel Application of the Evaluation Based on Distance from Average Solution (EDAS) Method Dharyll Prince M. Abellana , 1 Robert R. Roxas , 1 Demelo M. Lao , 1 Paula E. Mayol , 1 and Sanghyuk Lee 2 1 Department of Computer Science, College of Science, University of the Philippines Cebu, Cebu 6000, Philippines 2 Department of Mechatronics and Robotics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China Correspondence should be addressed to Dharyll Prince M. Abellana; dmabellana@up.edu.ph Received 30 March 2022; Revised 23 July 2022; Accepted 3 August 2022; Published 5 September 2022 Academic Editor: Adiel T. de Almeida-Filho Copyright © 2022 Dharyll Prince M. Abellana et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Combining filters in an ensemble to improve feature selection performance is a growing field in the literature. Current techniques, however, are focused on approaches that suffer from drawbacks such as sensitivity to skewed distribution, among others. To address this gap, this paper investigates the applicability of multiple criteria decision-making in ensemble feature selection. is paper adopts the Evaluation based on Distance from Average Solution (EDAS) method due to its many familiar elements to the feature selection community. An experiment was performed on six datasets and a control group. e paper uses the six datasets as levels of the blocking factor. A negative control group (i.e., no feature selection) was adopted to compare with the proposed algorithm. Results show that the proposed ensemble FS algorithm was able to reduce the dataset without compromising the performance of the classifier. e findings in this study would contribute to the literature in several ways. First, the paper is one of the few works to demonstrate how MCDM can be used in feature selection with promising results. Second, this paper is one of the few works to demonstrate the significance of including datasets as levels of a blocking factor when performing significance testing. Finally, 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. 1. Introduction e curse of dimensionality is a phenomenon that arises when analyzing data in high-dimensional spaces [1]. In classification, its major consequences are poor classification performance, noisy data, and hard-to-generalize results, to name a few [1]. Many scholars consider dimensionality reduction as the most straightforward approach to addressing the curse of dimensionality [2]. In the current literature, there are two major ways for performing di- mensionality reduction: (i) feature selection (FS) and (ii) feature projection (FP) [2]. FP transforms data from high- dimensional space to a lower-dimensional space while retaining the relationships between the original features [3]. For example, principal component analysis generates new features using the linear combinations of the original fea- tures [3]. While this technique can be useful for summa- rizing the original features using fewer variables, the generated features are usually not straightforward to in- terpret. us, it is usually not preferred when interpretation is crucial in the modeling process. By contrast, FS selects a subset of the original features that best represents the original features [2]. For example, the features can be ranked according to their association with the class and retained only the top 10% in the reduced dataset. Because this ap- proach only selects a subset of the original features, inter- pretation of the model would be straightforward as no new features are created [2]. us, this approach is preferable in Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 4126536, 13 pages https://doi.org/10.1155/2022/4126536