Ensemble feature selection using Bonferroni, OWA and Induced OWA aggregation operators

Abstract

Feature selection becomes inevitable owing to a rapid increase in digital technology which permits the generation of high dimensional data in a large quantity within a short time. Feature selection techniques not only improves classification accuracy but also decreases time complexity as well as computation cost and storage. Ensemble feature selection has lately emerged as a potential approach to data mining. Identifying numerous optimal features is one of the key advantages of ensemble feature selection. The objective of this study is to introduce an ensemble technique based on the rank aggregation procedure. Four operators are used for the purpose of aggregation, namely the induced ordered weighted averaging (IOWA) operator, the normalized Bonferroni weighted mean operator, the Bonferroni-ordered weighted averaging operator and the Bonferroni-induced ordered weighted averaging operator. These operators enable the evaluation of continuous aggregations, multiple assessments between each input and distance measurements in the same formulation. An objective weighting approach denoted as the entropy weight method, is utilized to measure the degree of disorder. A total of 10 benchmark data sets are employed to evaluate the superiority of the proposed methodologies. The effectiveness of the proposed method is evaluated and compared using the accuracy, F-measure, precision and recall performance metrics, and better results than those from other existing techniques are obtained.