Stability analysis of feature ranking techniques in the presence of noise: a comparative study Online publication date: Fri, 16-Oct-2020
by Iman Ramezani; Mojtaba Khorram Niaki; Milad Dehghani; Mostafa Rezapour
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 17, No. 4, 2020
Abstract: Noisy data is one of the common problems associated with real-world data, and may affects the performance of the data models, consequent decisions and the performance of feature ranking techniques. In this paper, we show how stability performance can be changed if different feature ranking methods against attribute noise and class noise are used. We consider Kendall's Tau rank correlation and Spearman rank correlation to evaluate various feature ranking methods stability, and quantify the degree of agreement between ordered lists of features created by a filter on a clean dataset and its outputs on the same dataset corrupted with different combinations of the noise level. According to the results of Kendall and Spearman measures, Gini index (GI) and information gain (IG) have the best performances respectively. Nevertheless, both Kendall and Spearman measures results show that ReliefF (RF) is the most sensitive (the worst) performance.
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