Comparison between Supervised and Unsupervised Feature Selection Methods

Lilli Haar, Katharina Anding, Konstantin Trambitckii, Gunther Notni

Abstract

The reduction of the feature set by selecting relevant features for the classification process is an important step within the image processing chain, but sometimes too little attention is paid to it. Such a reduction has many advantages. It can remove irrelevant and redundant data, improve recognition performance, reduce storage capacity requirements, computational time of calculations and also the complexity of the model. Within this paper supervised and unsupervised feature selection methods are compared with respect to the achievable recognition accuracy. Supervised Methods include information of the given classes in the selection, whereas unsupervised ones can be used for tasks without known class labels. Feature clustering is an unsupervised method. For this type of feature reduction, mainly hierarchical methods, but also k-means are used. Instead of this two clustering methods, the Expectation Maximization (EM) algorithm was used in this paper. The aim is to investigate whether this type of clustering algorithm can provide a proper feature vector using feature clustering. There is no feature reduction technique that provides equally best results for all datasets and classifiers. However, for all datasets, it was possible to reduce the feature set to a specific number of useful features without losses and often even with improvements in recognition performance.

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Paper Citation


in Harvard Style

Haar L., Anding K., Trambitckii K. and Notni G. (2019). Comparison between Supervised and Unsupervised Feature Selection Methods.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 582-589. DOI: 10.5220/0007385305820589


in Bibtex Style

@conference{icpram19,
author={Lilli Haar and Katharina Anding and Konstantin Trambitckii and Gunther Notni},
title={Comparison between Supervised and Unsupervised Feature Selection Methods},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={582-589},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007385305820589},
isbn={978-989-758-351-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Comparison between Supervised and Unsupervised Feature Selection Methods
SN - 978-989-758-351-3
AU - Haar L.
AU - Anding K.
AU - Trambitckii K.
AU - Notni G.
PY - 2019
SP - 582
EP - 589
DO - 10.5220/0007385305820589