- Title
- Feature-subspace aggregating: ensembles for stable and unstable learners
- Creator
- Ting, Kaiming; Wells, Jonathan; Tan, Swee; Teng, Shyh; Webb, Geoffrey
- Date
- 2011
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/35147
- Identifier
- vital:6395
- Identifier
-
https://doi.org/10.1007/s10994-010-5224-5
- Identifier
- ISSN:0885-6125
- Abstract
- This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds local models instead of global models. Feating is a generic ensemble approach that can enhance the predictive performance of both stable and unstable learners. In contrast, most existing ensemble approaches can improve the predictive performance of unstable learners only. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble through an increased level of localisation in Feating. Our empirical evaluation shows that Feating performs significantly better than Boosting, Random Subspace and Bagging in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by Feating makes feasible SVM ensembles that would otherwise be infeasible for large data sets. When SVM is the preferred base learner, we show that Feating SVM performs better than Boosting decision trees and Random Forests. We further demonstrate that Feating also substantially reduces the error of another stable learner, k-nearest neighbour, and an unstable learner, decision tree.
- Relation
- Machine Learning Vol. 82, no. 3 (2011), p. 375-397
- Rights
- Copyright Springer
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Classifier ensembles; Stable learners; Unstable learners; Model diversity; Local models; Global models; 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Science
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