A dynamic overproduce-and-choose strategy for the selection of classifier ensembles

  • Authors:

  • Eulanda M. Dos Santos;Robert Sabourin;Patrick Maupin

  • Affiliations:

  • ícole de technologie supérieure, ETS, 1100, Rue Notre-Dame Quest, Montreal, Que, Canada H3C1K3;ícole de technologie supérieure, ETS, 1100, Rue Notre-Dame Quest, Montreal, Que, Canada H3C1K3;Defence Research and Development Canada (DRDC Valcartier), Canada

  • Venue:
  • Pattern Recognition
  • Year:
  • 2008

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Abstract

The overproduce-and-choose strategy, which is divided into the overproduction and selection phases, has traditionally focused on finding the most accurate subset of classifiers at the selection phase, and using it to predict the class of all the samples in the test data set. It is therefore, a static classifier ensemble selection strategy. In this paper, we propose a dynamic overproduce-and-choose strategy which combines optimization and dynamic selection in a two-level selection phase to allow the selection of the most confident subset of classifiers to label each test sample individually. The optimization level is intended to generate a population of highly accurate candidate classifier ensembles, while the dynamic selection level applies measures of confidence to reveal the candidate ensemble with the highest degree of confidence in the current decision. Experimental results conducted to compare the proposed method to a static overproduce-and-choose strategy and a classical dynamic classifier selection approach demonstrate that our method outperforms both these selection-based methods, and is also more efficient in terms of performance than combining the decisions of all classifiers in the initial pool.