Stability analysis of Feature Ranking Techniques in the presence of noise: A comparative study

Autor:	Milad Dehghani, Mostafa Rezapour, Iman Ramezani, Mojtaba Khorram Niaki
Rok vydání:	2019
Předmět:	Noise Information Systems and Management Index (economics) Computer science Statistics Stability (learning theory) Filter (signal processing) Statistics Probability and Uncertainty Spearman's rank correlation coefficient Feature ranking Management Information Systems Data modeling Rank correlation
Zdroj:	International Journal of Business Intelligence and Data Mining. 1:1
ISSN:	1743-8195 1743-8187
DOI:	10.1504/ijbidm.2019.10012557
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb322f7aee6283242db483491b576ddf https://doi.org/10.1504/ijbidm.2019.10012557 Zobrazit plný text záznamu