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Structured Sparse Boosting for Graph Classification

Structured Sparse Boosting for Graph Classification Structured Sparse Boosting for Graph Classification HONGLIANG FEI and JUN HUAN, EECS Department, University of Kansas Boosting is a highly effective algorithm that produces a linear combination of weak classifiers (a.k.a. base learners) to obtain high-quality classification models. In this article, we propose a generalized logit boost algorithm in which base learners have structural relationships in the functional space. Although such relationships are generic, our work is particularly motivated by the emerging topic of pattern-based classification for semistructured data including graphs. Toward an efficient incorporation of the structure information, we have designed a general model in which we use an undirected graph to capture the relationship of subgraphbased base learners. In our method, we employ both L1 and Laplacian-based L2 regularization to logit boosting to achieve model sparsity and smoothness in the functional space spanned by the base learners. We have derived efficient optimization algorithms based on coordinate descent for the new boosting formulation and theoretically prove that it exhibits a natural grouping effect for nearby spatial or overlapping base learners and that the resulting estimator is consistent. Additionally, motivated by the connection between logit boosting and logistic regression, we extend our structured sparse regularization framework to logistic http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Structured Sparse Boosting for Graph Classification

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/2629328
Publisher site
See Article on Publisher Site

Abstract

Structured Sparse Boosting for Graph Classification HONGLIANG FEI and JUN HUAN, EECS Department, University of Kansas Boosting is a highly effective algorithm that produces a linear combination of weak classifiers (a.k.a. base learners) to obtain high-quality classification models. In this article, we propose a generalized logit boost algorithm in which base learners have structural relationships in the functional space. Although such relationships are generic, our work is particularly motivated by the emerging topic of pattern-based classification for semistructured data including graphs. Toward an efficient incorporation of the structure information, we have designed a general model in which we use an undirected graph to capture the relationship of subgraphbased base learners. In our method, we employ both L1 and Laplacian-based L2 regularization to logit boosting to achieve model sparsity and smoothness in the functional space spanned by the base learners. We have derived efficient optimization algorithms based on coordinate descent for the new boosting formulation and theoretically prove that it exhibits a natural grouping effect for nearby spatial or overlapping base learners and that the resulting estimator is consistent. Additionally, motivated by the connection between logit boosting and logistic regression, we extend our structured sparse regularization framework to logistic

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Aug 25, 2014

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