Scalable Feature Selection for Multi-class Problems
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 classifica tion tasks but often underperform on multi-class tasks. We suggest that they suffer from the so-called accum ulative effect which becomes more visible with the growing number of classes and results in removing relevan t and unredundant features. To remedy the problem, we propose two new feature filtering methods which are bo th scalable and well adapted for the multi-class cases. We report the evaluation results on 17 different dat asets which include both binary and multi-class cases.