| Autor: | David G. T. Denison |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
Statistics and Probability
Boosting (machine learning) Computational complexity theory business.industry Bayesian probability Decision tree Pattern recognition BrownBoost Theoretical Computer Science Bayes' theorem ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Alternating decision tree Artificial intelligence Gradient boosting Statistics Probability and Uncertainty business Mathematics |
| Zdroj: | Statistics and Computing. 11:171-178 |
| ISSN: | 0960-3174 |
| DOI: | 10.1023/a:1008931416845 |
| Popis: | Boosting is a new, powerful method for classification. It is an iterative procedure which successively classifies a weighted version of the sample, and then reweights this sample dependent on how successful the classification was. In this paper we review some of the commonly used methods for performing boosting and show how they can be fit into a Bayesian setup at each iteration of the algorithm. We demonstrate how this formulation gives rise to a new splitting criterion when using a domain-partitioning classification method such as a decision tree. Further we can improve the predictive performance of simple decision trees, known as stumps, by using a posterior weighted average of them to classify at each step of the algorithm, rather than just a single stump. The main advantage of this approach is to reduce the number of boosting iterations required to produce a good classifier with only a minimal increase in the computational complexity of the algorithm. |
| Databáze: | OpenAIRE |
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