EROS: Ensemble rough subspaces

Qinghua Hu

doi:10.1016/j.patcog.2007.04.022

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EROS: Ensemble rough subspaces

2007-12-01

PRE

DOI：10.1016/j.patcog.2007.04.022

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Abstract

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Ensemble learning is attracting much attention from pattern recognition and machine learning domains for good generalization. Both theoretical and experimental researches show that combining a set of accurate and diverse classifiers will lead to a powerful classification system. An algorithm, called FS-PP-EROS, for selective ensemble of rough subspaces is proposed in this paper. Rough set-based attribute reduction is introduced to generate a set of reducts, and then each reduct is used to train a base classifier. We introduce an accuracy-guided forward search and post-pruning strategy to select part of the base classifiers for constructing an efficient and effective ensemble system. The experiments show that classification accuracies of ensemble systems with accuracy-guided forward search strategy will increase at first, arrive at a maximal value, then decrease in sequentially adding the base classifiers. We delete the base classifiers added after the maximal accuracy. The experimental results show that the proposed ensemble systems outperform bagging and random subspace methods in terms of accuracy and size of ensemble systems. FS-PP-EROS can keep or improve the classification accuracy with very few base classifiers, which leads to a powerful and compact classification system. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keywords:

attribute reduction

ensemble learning

multiple classifier system

rough set

selective ensemble

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