{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:50:01Z","timestamp":1777697401909,"version":"3.51.4"},"reference-count":11,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2021,9,27]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3233\/idt-200186","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T13:13:11Z","timestamp":1631279591000},"page":"421-432","source":"Crossref","is-referenced-by-count":0,"title":["A straightforward feature selection method based on mean ratio for classifiers"],"prefix":"10.1177","volume":"15","author":[{"given":"Emmanuel","family":"Tavares","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alisson Marques","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gray Farias","family":"Moita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodrigo Tomas Nogueira","family":"Cardoso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-200186_ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-019-04218-6"},{"issue":"23","key":"10.3233\/IDT-200186_ref2","doi-asserted-by":"publisher","first-page":"12655","DOI":"10.1007\/s00500-019-03818-6","article-title":"A hybrid approach using rough set theory and hypergraph for feature selection on high-dimensional medical datasets","volume":"23","author":"Gauthama Raman","year":"2019","journal-title":"Soft Computing"},{"key":"10.3233\/IDT-200186_ref3","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/IDT-200186_ref5","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","article-title":"Selection of relevant features and examples in machine learning","volume":"97","author":"Blum","year":"1997","journal-title":"Artificial Intelligence"},{"key":"10.3233\/IDT-200186_ref6","doi-asserted-by":"publisher","DOI":"10.1002\/9781119515326.ch5"},{"issue":"5","key":"10.3233\/IDT-200186_ref7","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/TFUZZ.2019.2892363","article-title":"Multiobjective evolutionary feature selection for fuzzy classification","volume":"27","author":"Jimenez","year":"2019","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"11","key":"10.3233\/IDT-200186_ref8","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1007\/s00500-013-1130-7","article-title":"Ensembles of change-point methods to estimate the change point in residual sequences","volume":"17","author":"Alippi","year":"2013","journal-title":"Soft Computing"},{"issue":"1","key":"10.3233\/IDT-200186_ref12","doi-asserted-by":"crossref","first-page":"182","DOI":"10.2307\/2527626","article-title":"A non-parametric test for randomness in a sequence of multinomial trials","volume":"20","author":"Bennett","year":"1964","journal-title":"Biometrics"},{"key":"10.3233\/IDT-200186_ref17","doi-asserted-by":"crossref","first-page":"151482","DOI":"10.1109\/ACCESS.2019.2947701","article-title":"Combining multiple feature-ranking techniques and clustering of variables for feature selection","volume":"7","author":"Haq","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/IDT-200186_ref18","doi-asserted-by":"crossref","first-page":"15087","DOI":"10.1109\/ACCESS.2018.2815606","article-title":"Supervised feature selection with a stratified feature weighting method","volume":"6","author":"Chen","year":"2018","journal-title":"IEEE Access"},{"key":"10.3233\/IDT-200186_ref19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.patrec.2019.12.022","article-title":"An efficient unsupervised feature selection procedure through feature clustering","volume":"131","author":"Yan","year":"2020","journal-title":"Pattern Recognition Letters"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-200186","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:22:57Z","timestamp":1777454577000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-200186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":11,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/idt-200186","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,27]]}}}