Energy-Based metric for ensemble selection

Authors:
Weimei Zhi;Huaping Guo;Ming Fan
Affiliations:
School of Information Engineering, Zhengzhou University, P.R. China;School of Information Engineering, Zhengzhou University, P.R. China;School of Information Engineering, Zhengzhou University, P.R. China
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

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Abstract

Ensemble selection copes with the reduction of an ensemble of the predictive models to reduce its response time and increase its accuracy. A number of selection methods via greedy search of the space of all possible ensemble subsets have been recently proposed. The major issue of these algorithms is to construct an effective metric to supervise the search process. In this paper, we view the issue of ensemble problem from a new viewpoint: energy-based learning, and then contribute a novel metric called EBM (Energy-based Metric) to guide the search. Also, this metric takes into account the strength of the decision of the current ensemble. Empirical results show that using the proposed metric to select subensemble leads to significantly better accuracy results compared to state-of-the-art metrics.