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

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