Title

An ant colony optimization approach for stacking ensemble

Document Type

Conference paper

Source Publication

Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010

Publication Date

1-1-2010

First Page

146

Last Page

151

Keywords

ACO, Data mining, Ensemble, Metaheuristic, Stacking

Abstract

an ensemble in data mining is the strategy that combines a set of different classifiers together to generate an integrated classification system to classify new instances. In the early research, an ensemble outperforms any of its individual components. Stacking is one of the most influential ensemble among the proposed ensemble schemes. Stacking applies a two-level structure: the base-level classifiers output their own predictions and the meta-level classifier takes the outputs as its input to generate final decision. Most of the existing studies focus on the meta-level classifier adoption, and few on the topic about determining the configuration of both base-level classifiers and the meta-level classifier together. This work is inspired by the Ant Colony Optimization which is good at solving combinatorial optimization problems. We propose an ACO-Stacking ensemble approach and also perform some preliminary experiments to compare our approach with some well-known ensembles. The preliminary results show that the performance of the ACO-Stacking is promising.

DOI

10.1109/NABIC.2010.5716282

ISBN

9781424473762

Publisher Statement

Copyright © 2010 IEEE. Access to external full text or publisher's version may require subscription.

Full-text Version

Publisher’s Version

Recommended Citation

Chen, Y., & Wong, M. L. (2010). An ant colony optimization approach for stacking ensemble. In 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC), Fukuoka, 2010 (pp.146-151). doi: 10.1109/NABIC.2010.5716282

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