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Novel Layered Clustering-Based Approach for Generating Ensemble of Classifiers

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2 Author(s)
Ashfaqur Rahman ; Central Queensland University, Rockhampton, Australia ; Brijesh Verma

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

Published in:

IEEE Transactions on Neural Networks  (Volume:22 ,  Issue: 5 )