A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection in model-based classification have been proposed. Surprisingly, the impact of outliers and wrongly labeled units on the determination of relevant predictors has received far less attention, with almost no dedicated methodologies available in the literature. In the present paper, we introduce two robust variablearXiv:2007.14810v2 fatcat:ni3rtqut7bgddiaudv7tdvxuau