On the Improvement of Feature Selection Techniques: The Fitness Filter
Figueiredo, M. A. T.
On the Improvement of Feature Selection Techniques: The Fitness Filter, Proc INSTICC International Conf. on Pattern Recognition Applications and Methods - ICPRAM, Conference Online, Vol. , pp. - , February, 2021.
Digital Object Identifier:
Download Full text PDF ( 1 MB)
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.