Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection
The Journal of Machine Learning Research
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
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Feature selection is an important component of many machine learning applications. In this paper, we propose a new robust feature selection method for multi-class multi-label learning. In particular, feature correlation is added into the sparse learning of feature selection so that we can learn the feature correlation and do feature selection simultaneously. An efficient algorithm is introduced with rapid convergence. Our regression based objective makes the feature selection process more efficient. Experiments on benchmark data sets illustrate that the proposed method outperforms many state-of-the-art feature selection methods.