Abstract
Classifier is used for pattern recognition in various fields including data mining. Boosting is an ensemble learning method to boost (enhance) an accuracy of single classifier. We propose a new, robust boosting method by using a zero-one step function as a loss function. In deriving the method, the MarginBoost technique is blended with the stochastic gradient approximation algorithm, called Stochastic Noise Reaction (SNR). Based on intensive numerical experiments, we show that the proposed method is actually better than AdaBoost on test error rates in the case of noisy, mislabeled situation.
| Original language | English |
|---|---|
| Pages (from-to) | 95-110 |
| Number of pages | 16 |
| Journal | Journal of the Operations Research Society of Japan |
| Volume | 51 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2008 Mar |
| Externally published | Yes |
Keywords
- AdaBoost
- Data Analysis
- Data mining
- Stochastic noise reaction
- Zero-one loss function
ASJC Scopus subject areas
- Decision Sciences(all)
- Management Science and Operations Research
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Sano, N., Suzuki, H., & Koda, M. (2008). A robust ensemble learning using zero-one loss function. Journal of the Operations Research Society of Japan, 51(1), 95-110. https://doi.org/10.15807/jorsj.51.95