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PKDD
2005
Springer

Ensembles of Balanced Nested Dichotomies for Multi-class Problems

13 years 9 months ago
Ensembles of Balanced Nested Dichotomies for Multi-class Problems
Abstract. A system of nested dichotomies is a hierarchical decomposition of a multi-class problem with c classes into c − 1 two-class problems and can be represented as a tree structure. Ensembles of randomlygenerated nested dichotomies have proven to be an effective approach to multi-class learning problems [1]. However, sampling trees by giving each tree equal probability means that the depth of a tree is limited only by the number of classes, and very unbalanced trees can negatively affect runtime. In this paper we investigate two approaches to building balanced nested dichotomies—class-balanced nested dichotomies and data-balanced nested dichotomies—and evaluate them in the same ensemble setting. Using C4.5 decision trees as the base models, we show that both approaches can reduce runtime with little or no effect on accuracy, especially on problems with many classes. We also investigate the effect of caching models when building ensembles of nested dichotomies.
Lin Dong, Eibe Frank, Stefan Kramer
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PKDD
Authors Lin Dong, Eibe Frank, Stefan Kramer
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