- Title
- Local models - the key to boosting stable learners successfully
- Creator
- Ting, Kaiming; Zhu, Lian; Wells, Jonathan
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/70628
- Identifier
- vital:6521
- Identifier
-
https://doi.org/10.1111/j.1467-8640.2012.00441.x
- Identifier
- ISSN:1467-8640
- Abstract
- Boosting has been shown to improve the predictive performance of unstable learners such as decision trees, but not of stable learners like Support Vector Machines (SVM), k-nearest neighbours and Naive Bayes classifiers. In addition to the model stability problem, the high time complexity of some stable learners such as SVM prohibits them from generating multiple models to form an ensemble for large data sets. This paper introduces a simple method that not only enables Boosting to improve the predictive performance of stable learners, but also significantly reduces the computational time to generate an ensemble of stable learners such as SVM for large data sets that would otherwise be infeasible. The method proposes to build local models, instead of global models; and it is the first method, to the best of our knowledge, to solve the two problems in Boosting stable learners at the same time. We implement the method by using a decision tree to define local regions and build a local model for each local region. We show that this implementation of the proposed method enables successful Boosting of three types of stable learners: SVM, k-nearest neighbours and Naive Bayes classifiers.
- Relation
- Computational Intelligence Vol. 29, no. 2 (2013), p. 331-356
- Rights
- Copyright Elsevier
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0802 Computation Theory and Mathematics; 1702 Cognitive Science; Boosting; Local models; Stable learners
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