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Collective-agreement-based pruning of ensembles

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  • Rokach, Lior

Abstract

Ensemble methods combine several individual pattern classifiers in order to achieve better classification. The challenge is to choose the minimal number of classifiers that achieve the best performance. An ensemble that contains too many members might incur large storage requirements and even reduce the classification performance. The goal of ensemble pruning is to identify a subset of ensemble members that performs at least as good as the original ensemble and discard any other members. In this paper, we introduce the Collective-Agreement-based Pruning (CAP) method. Rather than ranking individual members, CAP ranks subsets by considering the individual predictive ability of each member along with the degree of redundancy among them. Subsets whose members highly agree with the class while having low inter-agreement are preferred.

Suggested Citation

  • Rokach, Lior, 2009. "Collective-agreement-based pruning of ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1015-1026, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1015-1026
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    References listed on IDEAS

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    1. Moskovitch, Robert & Elovici, Yuval & Rokach, Lior, 2008. "Detection of unknown computer worms based on behavioral classification of the host," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4544-4566, May.
    2. Menahem, Eitan & Shabtai, Asaf & Rokach, Lior & Elovici, Yuval, 2009. "Improving malware detection by applying multi-inducer ensemble," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1483-1494, February.
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    Cited by:

    1. Radhakrishnan Nagarajan & Craig S Miller & Dolph Dawson III & Mohanad Al-Sabbagh & Jeffrey L Ebersole, 2015. "Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-15, September.
    2. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
    3. Lior Rokach, 2012. "Applying the Publication Power Approach to Artificial Intelligence Journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(6), pages 1270-1277, June.

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