Applying ant colony optimization to configuring stacking ensembles for data mining

YiJun CHEN, Man Leung WONG, Haibing LI

Research output: Journal PublicationsJournal Article (refereed)peer-review

52 Citations (Scopus)


An ensemble is a collective decision-making system which applies a strategy to combine the predictions of learned classifiers to generate its prediction of new instances. Early research has proved that ensemble classifiers in most cases can be more accurate than any single component classifier both empirically and theoretically. Though many ensemble approaches are proposed, it is still not an easy task to find a suitable ensemble configuration for a specific dataset. In some early works, the ensemble is selected manually according to the experience of the specialists. Metaheuristic methods can be alternative solutions to find configurations. Ant Colony Optimization (ACO) is one popular approach among metaheuristics. In this work, we propose a new ensemble construction method which applies ACO to the stacking ensemble construction process to generate domain-specific configurations. A number of experiments are performed to compare the proposed approach with some well-known ensemble methods on 18 benchmark data mining datasets. The approach is also applied to learning ensembles for a real-world cost-sensitive data mining problem. The experiment results show that the new approach can generate better stacking ensembles.
Original languageEnglish
Pages (from-to)2688-2702
Number of pages15
JournalExpert Systems with Applications
Issue number6
Early online date20 Nov 2013
Publication statusPublished - May 2014

Bibliographical note

This research is supported by General Research Fund LU310111 from the Research Grant Council of the Hong Kong Special Administrative Region.


  • ACO
  • Data mining
  • Direct marketing
  • Ensemble
  • Metaheuristics
  • Stacking


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