Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography

Research output: Contribution to journalReview articlepeer-review

191 Scopus citations

Abstract

Ensemble methodology, which builds a classification model by integrating multiple classifiers, can be used for improving prediction performance. Researchers from various disciplines such as statistics, pattern recognition, and machine learning have seriously explored the use of ensemble methodology. This paper presents an updated survey of ensemble methods in classification tasks, while introducing a new taxonomy for characterizing them. The new taxonomy, presented from the algorithm designer's point of view, is based on five dimensions: inducer, combiner, diversity, size, and members' dependency. We also propose several selection criteria, presented from the practitioner's point of view, for choosing the most suitable ensemble method.

Original languageEnglish
Pages (from-to)4046-4072
Number of pages27
JournalComputational Statistics and Data Analysis
Volume53
Issue number12
DOIs
StatePublished - 1 Oct 2009

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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