Authors:
Artur J. Ferreira
1
;
2
and
Mário A. T. Figueiredo
1
;
3
Affiliations:
1
Instituto de Telecomunicações, Lisboa, Portugal
;
2
ISEL, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Portugal
;
3
IST, Instituto Superior Técnico, Universidade de Lisboa, Portugal
Keyword(s):
Machine Learning, Feature Selection, Dimensionality Reduction, Relevance-Redundancy, Classification.
Abstract:
The need for feature selection (FS) techniques is central in many machine learning and pattern recognition problems. FS is a vast research field and therefore we now have many FS techniques proposed in the literature, applied in the context of quite different problems. Some of these FS techniques follow the relevance-redundancy (RR) framework to select the best subset of features. In this paper, we propose a supervised filter FS technique, named as fitness filter, that follows the RR framework and uses data discretization. This technique can be used directly on low or medium dimensional data or it can be applied as a post-processing technique to other FS techniques. Specifically, when used as a post-processing technique, it further reduces the dimensionality of the feature space found by common FS techniques and often improves the classification accuracy.