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Proceedings Paper

Comparative study of feature selection with ensemble learning using SOM variants
Author(s): Ameni Filali; Chiraz Jlassi; Najet Arous
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Paper Abstract

Ensemble learning has succeeded in the growth of stability and clustering accuracy, but their runtime prohibits them from scaling up to real-world applications. This study deals the problem of selecting a subset of the most pertinent features for every cluster from a dataset. The proposed method is another extension of the Random Forests approach using self-organizing maps (SOM) variants to unlabeled data that estimates the out-of-bag feature importance from a set of partitions. Every partition is created using a various bootstrap sample and a random subset of the features. Then, we show that the process internal estimates are used to measure variable pertinence in Random Forests are also applicable to feature selection in unsupervised learning. This approach aims to the dimensionality reduction, visualization and cluster characterization at the same time. Hence, we provide empirical results on nineteen benchmark data sets indicating that RFS can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art unsupervised methods, with a very limited subset of features. The approach proves promise to treat with very broad domains.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410Z (17 March 2017); doi: 10.1117/12.2268538
Show Author Affiliations
Ameni Filali, Ecole Nationale d'Ingénieurs de Tunis (Tunisia)
Chiraz Jlassi, Ecole Nationale d'Ingénieurs de Tunis (Tunisia)
Najet Arous, Ecole Nationale d'Ingénieurs de Tunis (Tunisia)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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