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Scalable Feature Selection for Multi-Class Problems

Boris Chidlovskii, Loïc Lecerf
Scalable feature selection algorithms should remove irrelevant and redundant features and scale well on very large datasets. We identify that the currently best state-of-art methods perform well on binary classification tasks but often underperform on multi-class tasks. We suggest that they suffer from the socalled accumulative effect which becomes more visible with the growing number of classes and results in removing relevant and unredundant features. To remedy the problem, we propose two new feature filtering methods which are both scalable and well adapted for the multi-class cases. We report the evaluation results on 17 different datasets which include both binary and multi-class cases.
ECML PKDD 2008 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), Antwerp, Belgium, September 15-19, 2008