gBoost: a mathematical programming approach to graph classification and regression

Saigo, Hiroto; Nowozin, Sebastian; Kadowaki, Tadashi; Kudo, Takeo; Tsuda, Koji

doi:10.1007/s10994-008-5089-z

Cited by 125 publications

(188 citation statements)

References 34 publications

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“…who report a best AUROC of 88.9% using gBoost, 43 and an accuracy of 82.5%. Kazius et al 33 achieved a training accuracy of 83% with a manually constructed model, using all data without cross-validation.…”

Section: Resultsmentioning

confidence: 98%

Molecular Graph Augmentation with Rings and Functional Groups

Grave

Costa

2010

J. Chem. Inf. Model.

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Molecular graphs are a compact representation of molecules, but may be too concise to obtain optimal generalization performance from graph-based machine learning algorithms. Over centuries, chemists have learned what are the important functional groups in molecules. This knowledge is normally not manifest in molecular graphs. In this paper, we introduce a simple method to incorporate this type of background knowledge: we insert additional vertices with corresponding edges for each functional group and ring structure identified in the molecule.We present experimental evidence that, on a wide range of ligand-based tasks and data sets, the proposed augmentation method improves the predictive performance over several graph kernel based QSAR models. When the augmentation technique is used with the recent Pairwise Maximal Common Subgraphs Kernel, we achieve a significant improvement over the current state-of-the-art on the NCI-60 cancer data set in 28 out of 60 cell lines, with the other 32 cell lines showing no significant difference in accuracy. Finally, on the Bursi mutagenicity data set, we obtain near-optimal predictions.

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Section: Resultsmentioning

confidence: 98%

Molecular Graph Augmentation with Rings and Functional Groups

Grave

Costa

2010

J. Chem. Inf. Model.

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“…Computing the edit distance between two objects can be done in polynomial time using Dynamic Programming, [232]. More complex structures, such as trees, or, even more generally, graphs, appear naturally in many real world data sets of biological sequences, or semi-structured texts such as HTML or XML, [206,126,246]. Computing in these cases a dissimilarity δ for a given pair of objects may lead to hard combinatorial optimization problems, [27].…”

Section: Nearest-neighbors Methodsmentioning

confidence: 99%

“…Optimization has shown to be useful to decide how classifiers should be ensembled. For instance, in [77,206] a column generation approach, [105], is used in the boosting environment, whereas a quadratic programming model is used in [174].…”

Section: Classification Treesmentioning

confidence: 99%

Supervised classification and mathematical optimization

Carrizosa

Morales

2013

Computers & Operations Research

124

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Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.

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“…More recently, substructure boosting approach has been successfully applied to different learning tasks on various kinds of data including RNA secondary structure clustering [9], video classification [10], and QSAR [11], [12]. These methods combine statistical learning algorithms with pattern mining algorithms to directly mine discriminative patterns which are optimal for the subsequent learning task in an iterative fashion [13].…”

Section: Introductionmentioning

confidence: 99%

Fast Iterative Mining Using Sparsity-Inducing Loss Functions

Saigo

Kashima

Tsuda³

2013

IEICE Trans. Inf. & Syst.

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SUMMARYApriori-based mining algorithms enumerate frequent patterns efficiently, but the resulting large number of patterns makes it difficult to directly apply subsequent learning tasks. Recently, efficient iterative methods are proposed for mining discriminative patterns for classification and regression. These methods iteratively execute discriminative pattern mining algorithm and update example weights to emphasize on examples which received large errors in the previous iteration. In this paper, we study a family of loss functions that induces sparsity on example weights. Most of the resulting example weights become zeros, so we can eliminate those examples from discriminative pattern mining, leading to a significant decrease in search space and time. In computational experiments we compare and evaluate various loss functions in terms of the amount of sparsity induced and resulting speed-up obtained.

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gBoost: a mathematical programming approach to graph classification and regression

Cited by 125 publications

References 34 publications

Molecular Graph Augmentation with Rings and Functional Groups

Molecular Graph Augmentation with Rings and Functional Groups

Supervised classification and mathematical optimization

Fast Iterative Mining Using Sparsity-Inducing Loss Functions

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