Heuristic Search-Based Stacking of Classifiers

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Author(s): Agapito Ledezma (Universidad Carlos III de Madrid, Spain), Ricardo Aler (Universidad Carlos III de Madrid, Spain)and Daniel Borrajo (Universidad Carlos III de Madrid, Spain)
Copyright: 2002
Pages: 14
Source title: Heuristic and Optimization for Knowledge Discovery
Source Author(s)/Editor(s): Hussein A. Abbass (University of New South Wales, Australia), Charles S. Newton (University of New South Wales, Australia)and Ruhul Sarker (University of New South Wales, Australia)
DOI: 10.4018/978-1-930708-26-6.ch004

Keywords: Data Mining and Databases / Engineering Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Currently, the combination of several classifiers is one of the most active fields within inductive learning. Examples of such techniques are boosting, bagging and stacking. From these three techniques, stacking is perhaps the least used one. One of the main reasons for this relates to the difficulty to define and parameterize its components: selecting which combination of base classifiers to use and which classifiers to use as the meta-classifier. The approach we present in this chapter poses this problem as an optimization task and then uses optimization techniques based on heuristic search to solve it. In particular, we apply genetic algorithms to automatically obtain the ideal combination of learning methods for the stacking system.

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