An Application of Boosting to Graph Classification

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How researchers see this paper

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Text Categorization as a Graph Classification Problem (2015, methodology): “The main idea is to mine frequent subgraphs and use them as features for classiﬁcation, be it with Adaboost (Kudo et al., 2004) or a linear SVM (Deshpande et al., 2005).”
Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, background): “Recent studies have proposed several effective graph classification methods, including substructure (or socalled fragment) based approaches [17, 8, 28], kernelbased approaches [16, 22, 12], and boosting methods [20].”
gBoost: a mathematical programming approach to graph classification and regression (2009, methodology): “Kudo et al. (2005) proposed to combine AdaBoost and the optimal pattern search algorithm.”
Sentiment Classification using Automatically Extracted Subgraph Features (2010, background): “Subgraph features combined with the feature evolution approach gives promising results, with an improvement in performance over the baseline.”
Weighted Substructure Mining for Image Analysis (2007, background): “To obtain the best pattern, the algorithm should be slightly modified such that τ is always updated to the current best value [7].”
Discriminative Subsequence Mining for Action Classification (2007, background): “While this is similar to the usual 1-vs-1 decomposition, the DDAG offers the additional advantage that we do not have to resolve ties during test time.”