Troika - An improved stacking schema for classification tasks

Eitan Menahem, Lior Rokach, Yuval Elovici

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Stacking is a general ensemble method in which a number of base classifiers are combined using one meta-classifier which learns their outputs. Such an approach provides certain advantages: simplicity; performance that is similar to the best classifier; and the capability of combining classifiers induced by different inducers. The disadvantage of stacking is that on multiclass problems, stacking seems to perform worse than other meta-learning approaches. In this paper we present Troika, a new stacking method for improving ensemble classifiers. The new scheme is built from three layers of combining classifiers. The new method was tested on various datasets and the results indicate the superiority of the proposed method to other legacy ensemble schemes, Stacking and StackingC, especially when the classification task consists of more than two classes.

Original languageEnglish
Pages (from-to)4097-4122
Number of pages26
JournalInformation Sciences
Volume179
Issue number24
DOIs
StatePublished - 15 Dec 2009

Keywords

  • Ensemble of classifiers
  • Machine learning
  • Meta combination
  • Stacked generalization

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