A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Complexity Measures of Supervised Classification Problems
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Linear Programming Boosting via Column Generation
Machine Learning
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DNA Sequence Classification Using DAWGs
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XRules: an effective structural classifier for XML data
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Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
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Direct mining of discriminative and essential frequent patterns via model-based search tree
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Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct Discriminative Pattern Mining for Effective Classification
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Classification of software behaviors for failure detection: a discriminative pattern mining approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Time series shapelets: a new primitive for data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Top-k interesting phrase mining in ad-hoc collections using sequence pattern indexing
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IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Exploring discriminative pose sub-patterns for effective action classification
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Application of tree-structured data mining for analysis of process logs in XML format
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Troubleshooting interactive complexity bugs in wireless sensor networks using data mining techniques
ACM Transactions on Sensor Networks (TOSN)
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Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. These algorithms assume that feature values of the mined patterns are binary, i.e., a pattern either exists or not. In some problems, however, the number of times a pattern appears is more informative than whether a pattern appears or not. To resolve these deficiencies, we propose a mathematical programming method that directly mines discriminative patterns as numerical features for classification. We also propose a novel search space shrinking technique which addresses the inefficiencies in iterative pattern mining algorithms. Finally, we show that our method is an order of magnitude faster, significantly more memory efficient and more accurate than current approaches.