2010 BoostingwithStructureInformatio

• (Fei et al., 2010) ⇒ Hongliang Fei, and Jun Huan. (2010). “Boosting with Structure Information in the Functional Space: An Application to Graph Classification.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835886

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Cited By

• http://scholar.google.com/scholar?q=%22Boosting+with+structure+information+in+the+functional+space%3A+an+application+to+graph+classification%22+2010
• http://portal.acm.org/citation.cfm?id=1835886&preflayout=flat#citedby

Quotes

Abstract

Boosting is a very successful classification algorithm that produces a linear combination of "weak" classifiers (a.k.a. base learners) to obtain high quality classification models. this paper we propose a new boosting algorithm where base learners have structure relationships in the functional space. Though such relationships are generic, our work is particularly motivated by the emerging topic of pattern based classification for semi-structured data including graphs. Towards an efficient incorporation of the structure information, we have designed a general model where we use an undirected graph to capture the relationship of subgraph-based base learners. In our method, we combine both [math]\displaystyle{ L_1 }[/math] norm and Laplacian based [math]\displaystyle{ L_2 }[/math] penalty with Logit loss function of Logit Boost. In this approach, we enforce model sparsity and smoothness in the functional space spanned by the basis functions. We have derived efficient optimization algorithms based on coordinate decent for the new boosting formulation and theoretically prove that it exhibits a natural grouping effect for nearby spatial or overlapping features. Using comprehensive experimental study, we have demonstrated the effectiveness of proposed learning methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 BoostingwithStructureInformatio	Hongliang Fei Jun Huan			Boosting with Structure Information in the Functional Space: An Application to Graph Classification		KDD-2010 Proceedings		10.1145/1835804.1835886		2010