Document Type
Journal article
Source Publication
Expert Systems With Applications
Publication Date
5-2014
Volume
41
Issue
6
First Page
2688
Last Page
2702
Keywords
ACO, Data mining, Direct marketing, Ensemble, Metaheuristics, Stacking
Abstract
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.
DOI
10.1016/j.eswa.2013.10.063
Print ISSN
09574174
E-ISSN
18736793
Publisher Statement
Copyright © 2013 Elsevier Ltd
Access to external full text or publisher's version may require subscription.
Full-text Version
Accepted Author Manuscript
Language
English
Recommended Citation
Chen, Y., Wong, M. L., & Li, H. (2014). Applying ant colony optimization to configuring stacking ensembles for data mining. Expert Systems with Applications, 41(6), 2688-2702. doi: 10.1016/j.eswa.2013.10.063