2014 AMultiClassBoostingMethodwithDi

• (Zhai et al., 2014) ⇒ Shaodan Zhai, Tian Xia, and Shaojun Wang. (2014). “A Multi-class Boosting Method with Direct Optimization.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623689

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+A+Multi-class+Boosting+Method+with+Direct+Optimization
• http://dl.acm.org/citation.cfm?id=2623330.2623689&preflayout=flat#citedby

Quotes

Author Keywords

• Boosting; classification; direct optimization; learning; noise tolerance; supervised learning

Abstract

We present a direct multi-class boosting (DMCBoost) method for classification with the following properties: (i) instead of reducing the multi-class classification task to a set of binary classification tasks, DMCBoost directly solves the multi-class classification problem, and only requires very weak base classifiers; (ii) DMCBoost builds an ensemble classifier by directly optimizing the non-convex performance measures, including the empirical classification error and margin functions, without resorting to any upper bounds or approximations. As a non-convex optimization method, DMCBoost shows competitive or better results than state-of-the-art convex relaxation boosting methods, and it performs especially well on the noisy cases.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 AMultiClassBoostingMethodwithDi	Shaojun Wang Tian Xia Shaodan Zhai			A Multi-class Boosting Method with Direct Optimization				10.1145/2623330.2623689		2014