Collective-agreement-based pruning of ensembles

نویسنده

  • Lior Rokach
چکیده

The main idea of ensemble methodology is to weigh several individual pattern classifiers, and combine them to reach a better classification performance. Nevertheless, some ensembles superfluously contain too many members, which results in large storage requirements and in some cases it may even reduce 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 as redundant members. In this paper we present the Collective Agreement-based Pruning (CAP) method. Rather than ranking individual member, CAP ranks the worth of ensemble 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.

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عنوان ژورنال:
  • Computational Statistics & Data Analysis

دوره 53  شماره 

صفحات  -

تاریخ انتشار 2009