Boosting for Correlated Binary Classification
| Autor: | Irina Dinu, Adeniyi J. Adewale, Yutaka Yasui |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Statistics and Probability
Boosting (machine learning) business.industry Binary number Pattern recognition Matched pair Binary classification Cluster (physics) Discrete Mathematics and Combinatorics Artificial intelligence Statistics Probability and Uncertainty Gradient descent business Independent data LogitBoost Mathematics |
| Zdroj: | Journal of Computational and Graphical Statistics. 19:140-153 |
| ISSN: | 1537-2715 1061-8600 |
| Popis: | Boosting is a successful method for dealing with problems of high-dimensional classification of independent data. However, existing variants do not address the correlations in the context of longitudinal or cluster study-designs with measurements collected across two or more time points or in clusters. This article presents two new variants of boosting with a focus on high-dimensional classification problems with matched-pair binary responses or, more generally, any correlated binary responses. The first method is based on the generic functional gradient descent algorithm and the second method is based on a direct likelihood optimization approach. The performance and the computational requirements of the algorithms were evaluated using simulations. Whereas the performance of the two methods is similar, the computational efficiency of the generic-functional-gradient-descent-based algorithm far exceeds that of the direct-likelihood-optimization-based algorithm. The former method is illustrated using data on... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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