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Advisor(s)
Abstract(s)
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.
Description
Keywords
Machine learning Feature selection Dimensionality reduction Relevance-redundancy Classification
Citation
FERREIRA, Artur J.; FIGUEIREDO, Mário A. T. – On the improvement of feature selection techniques: the fitness filter. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM). Vienna, Áustria: Scitepress, 2021. ISBN 978-989-758-486-2. Pp. 365-372
Publisher
Scitepress