Affiliations: Graduate Program in Mathematical and Computational Modeling, CEFET-MG – Federal Center for Technological Education of Minas Gerais, MG, Brazil
Correspondence:
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Corresponding author: Emmanuel Tavares, Graduate Program in Mathematical and Computational Modeling, CEFET-MG – Federal Center for Technological Education of Minas Gerais, Av. Amazonas, 7675 – Nova Gameleira, 30510-000, Belo Horizonte, MG, Brazil. E-mail: emmanuelcomp@gmail.com.
Abstract: Feature Selection (FS) is currently a very important and prominent research area. The focus of FS is to identify and to remove irrelevant and redundant features from large data sets in order to reduced processing time and to improve the predictive ability of the algorithms. Thus, this work presents a straightforward and efficient FS method based on the mean ratio of the attributes (features) associated with each class. The proposed filtering method, here called MRFS (Mean Ratio Feature Selection), has only equations with low computational cost and with basic mathematical operations such as addition, division, and comparison. Initially, in the MRFS method, the average from the data sets associated with the different outputs is computed for each attribute. Then, the calculation of the ratio between the averages extracted from each attribute is performed. Finally, the attributes are ordered based on the mean ratio, from the smallest to the largest value. The attributes that have the lowest values are more relevant to the classification algorithms. The proposed method is evaluated and compared with three state-of-the-art methods in classification using four classifiers and ten data sets. Computational experiments and their comparisons against other feature selection methods show that MRFS is accurate and that it is a promising alternative in classification tasks.
Keywords: Mean ratio, feature selection, filter method, classification, computational intelligence
