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| Titel: |
iBoost: Boosting using an instance-based exponential weighting scheme |
| Auteur: |
Karmaker, Amitava Yoon, Kihoon Nguyen, Chau Kwek, Stephen |
| Verschenen in: |
International journal of hybrid intelligent systems |
| Paginering: | Jaargang 4 (2007) nr. 4 pagina's 243-254 |
| Jaar: |
2007-12-26 |
| Inhoud: |
AdaBoost is a well-recognized ensemble method to improve prediction accuracy over the base learning algorithm. However, it is prone to overfitting the training instances [18]. Freund, Mansour and Schapire [5] established that using exponential weighting scheme in combining classifiers reduces the problem of overfitting. Also, Helmbold, Kwek and Pitt [7] showed in the prediction using a pool of experts framework an instance-based weighting scheme improves performance. Motivated by these results, we propose here an instance-based exponential weighting scheme in which the weights of the base classifiers are adjusted according to the test instance x. Here, a competency classifier c_i is constructed for each base classifier h_i to predict whether the base classifier's guess of x's label can be trusted and adjust the weight of h_i accordingly. We show that this instance-based exponential weighting scheme enhances the performance of AdaBoost. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
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