Effective Feature Selection on Data with Uncertain Labels

Nowadays, various learning technologies are required on uncertain data. As an important pre-processing step in data mining, feature selection needs to consider this vagueness or uncertainty. In this paper, we propose a novel algorithm to evaluate the correlation between features and uncertain class labels on the basis of Hilbert-Schmidt Independence Criterion. Consequently, the features can be ranked according to this criterion. Experimental results on extensive datasets demonstrate the benefits of our method.