Dynamic Ensemble Selection Based on Rough Set Reduction and Cluster Matching

Ying-Chun CHEN  Ou LI  Yu SUN  

IEICE TRANSACTIONS on Communications   Vol.E101-B   No.10   pp.2196-2202
Publication Date: 2018/10/01
Publicized: 2018/04/11
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017EBP3441
Type of Manuscript: PAPER
Category: Fundamental Theories for Communications
mutual information,  rough set,  attribute reduction,  dynamic ensemble selection,  k-means,  

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Ensemble learning is widely used in the field of sensor network monitoring and target identification. To improve the generalization ability and classification precision of ensemble learning, we first propose an approximate attribute reduction algorithm based on rough sets in this paper. The reduction algorithm uses mutual information to measure attribute importance and introduces a correction coefficient and an approximation parameter. Based on a random sampling strategy, we use the approximate attribute reduction algorithm to implement the multi-modal sample space perturbation. To further reduce the ensemble size and realize a dynamic subset of base classifiers that best matches the test sample, we define a similarity parameter between the test samples and training sample sets that takes the similarity and number of the training samples into consideration. We then propose a k-means clustering-based dynamic ensemble selection algorithm. Simulations show that the multi-modal perturbation method effectively selects important attributes and reduces the influence of noise on the classification results. The classification precision and runtime of experiments demonstrate the effectiveness of the proposed dynamic ensemble selection algorithm.