Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Advances in Component-Based Face Detection
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
A rank sum test method for informative gene discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Significance of Gene Ranking for Classification of Microarray Samples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
RV-SVM: An Efficient Method for Learning Ranking SVM
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
HyParSVM: a new hybrid parallel software for support vector machine learning on SMP clusters
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Data mining with parallel support vector machines for classification
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
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Nowadays, high throughput experimental techniques make it feasible to examine and collect massive dataat the molecular level.These data, typically mappedto a very high dimensional feature space, carry richinformation about functionalities of certain chemicalor biological entities and can be used to infer valuableknowledge for the purposes of classification and prediction.Typically, a small number of features or featurecombinations may play determinant roles in functionaldiscrimination.The identification of such features orfeature combinations is of great importance.In this paper,we study the problem of discovering compact andhighly discriminative features or feature combinationsfrom a rich feature collection.We employ the supportvector machine as the classification means and aim atfinding compact feature combinations.Comparing toprevious methods on feature selection, which identifyfeatures solely based on their individual roles in theclassification, our method is able to identify minimalfeature combinations that ultimately have determinantroles in a systematic fashion.Experimental study ondrug activity data shows that our method can discoverdescriptors that are not necessarily significant individuallybut are most significant collectively.