Kamalov, FiruzThabtah, Fadi2020-12-122020-12-12© 20172017-12-01Kamalov, F. &Thabtah, F. (2017). A Feature Selection Method Based on Ranked Vector Scores of Features for Classification. Annals of Data Science 4(1), 483–502. https://doi.org/10.1007/s40745-017-0116-121985804https://doi.org/10.1007/s40745-017-0116-1http://hdl.handle.net/20.500.12519/300This article is not available at CUD collection. The version of scholarly record of this article is published in Annals of Data Science (2017), available online at: https://doi.org/10.1007/s40745-017-0116-1One of the major aspects of any classification process is selecting the relevant set of features to be used in a classification algorithm. This initial step in data analysis is called the feature selection process. Disposing of the irrelevant features from the dataset will reduce the complexity of the classification task and will increase the robustness of the decision rules when applied on the test set. This paper proposes a new filtering method that combines and normalizes the scores of three major feature selection methods: information gain, chi-squared statistic and inter-correlation. Our method utilizes the strengths of each of the aforementioned methods to maximum advantage while avoiding their drawbacks—especially the disparity of the results produced by these methods. Our filtering method stabilizes each variable score and gives it the true rank among the input data’s available variables. Hence it maximizes the stability in the variables’ scores without losing the overall accuracy of the predictive model. A number of experiments on different datasets from various domains have shown that features chosen by the proposed method are highly predictive when compared with features selected by other existing filtering methods. The evaluation of the filtering phase was conducted via thorough experimentations using a number of predictive classification algorithms in addition to statistical analysis of the filtering methods’ scores. © 2017, Springer-Verlag GmbH Germany.enPermission to reuse the abstract has been secured from Springer Science and Business Media Deutschland GmbHClassification accuracyData miningDimensionality reductionFeature selectionPredictive modelsRanking of featuresArticleCopyright : © 2017, Springer-Verlag GmbH Germany