A spectral clustering based ensemble pruning approach

• We employ spectral clustering to prune classifiers in an ensemble.
• A classifier similarity concept is defined and used for classifier pruning.
• The similarity takes into account the predictive performance and the diversity.
• Experimental results indicate the effectiveness of the proposed approach.

This paper introduces a novel bagging ensemble classifier pruning approach. Most investigated pruning approaches employ heuristic functions to rank classifiers in the ensemble, and select part of them from the ranked ensemble, so redundancy may exist in the selected classifiers. Based on the idea that the selected classifiers should be accurate and diverse, we define classifier similarity according to the predictive accuracy and the diversity, and introduce a Spectral Clustering based classifier selection approach (SC). SC groups the classifiers into two clusters based on the classifier similarity, and retains one cluster of classifiers in the ensemble. Experimental results show that SC is competitive in terms of classification accuracy.