@inproceedings{2000207,
author={Annalisa Appice and  Michelangelo Ceci and  Simon Rawles and Peter Flach},
title={Redundant feature elimination for multi-class problems},
booktitle={Proceedings of the 21st International Conference on Machine Learning (ICML 2004)},
editor={Russ Greiner  and Dale Schuurmans},
ISBN={1-58113-838-5},
publisher={ACM},
pages={33--40},
month={July},
year={2004},
abstract={
We consider the problem of eliminating redundant Boolean
	features for a given data set, where a feature is redundant if it
	separates the classes less well than another feature or set of
	features. Lavrac et al. proposed the algorithm REDUCE that works
	by pairwise comparison of features, i.e., it eliminates a feature if
	it is redundant with respect to another feature. Their algorithm
	operates in an ILP setting and is restricted to two-class problems.
	In this paper we improve their method and extend it to multiple
	classes. Central to our approach is the notion of a neighbourhood
	of examples: a set of examples of the same class where the number
	of different features between examples is relatively small. Redundant
	features are eliminated by applying a revised version of the REDUCE
	method to each pair of neighbourhoods of different class. We analyse
	the performance of our method on a range of data sets.},
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000207},
url={http://www.cs.bris.ac.uk/Publications/Papers/2000207.pdf},
keyword={Artificial Intelligence,Machine Learning},
pubtype={102}
}