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  Mathematical Programming Methods for Learning from Structured Data: Applications to Molecular QSAR Analysis, HIV Drug Resistance Prediction, and Image Categorisation

Tsuda, K., Saigo, H., Nowozin, S., Kudo, T., & Uno, T. (2007). Mathematical Programming Methods for Learning from Structured Data: Applications to Molecular QSAR Analysis, HIV Drug Resistance Prediction, and Image Categorisation. Talk presented at Joint Bioinformatics Education Program: Kyoto University and University of Tokyo. Kyoto, Japan. 2007-05-14.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-4A31-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-4A32-1
Genre: Talk

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 Creators:
Tsuda, K1, 2, Author              
Saigo, H1, 2, Author              
Nowozin, S1, 2, Author              
Kudo , T, Author
Uno, T, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In learning from structured data such as graphs, trees and itemsets, frequent substructure mining methods are used for deriving features. However, frequent patterns are not necessarily informative for the given learning problem. We propose a mathematical programming boosting method that progressively collects informative patterns. Compared to AdaBoost, our method can build the prediction rule with fewer iterations. To apply the boosting method to graph data, a branch-and-bound-type pattern search algorithm is developed based on the DFS code tree. The constructed search space is reused in later iterations to minimize the computation time. Our method can learn more efficiently than the simpler method based on frequent substructure mining, because the output labels are used as an extra information source for pruning the search space. Furthermore, by engineering the mathematical program, a wide range of machine learning problems can be solved without modifying the pattern search algorithm. I will show practical applications to graphs (QSAR) and itemsets (HIV and images).

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 Dates: 2007-05
 Publication Status: Published online
 Pages: -
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Title: Joint Bioinformatics Education Program: Kyoto University and University of Tokyo
Place of Event: Kyoto, Japan
Start-/End Date: 2007-05-14
Invited: Yes

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