Reference : Learning from positive and unlabeled examples by enforcing statistical significance
Scientific congresses and symposiums : Paper published in a journal
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/87877
Learning from positive and unlabeled examples by enforcing statistical significance
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Apr-2011
JMLR: Workshop and Conference Proceedings
15
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics
Yes
No
International
1938-7228
Fourteenth International Conference on Artificial Intelligence and Statistics
April 11-13
Geoffrey Gordon, David Dunson, and Miroslav Dudík
Miami
USA
[en] machine learning ; kernel methods ; semi-supervised learning ; bioinformatics ; gene regulatory networks
[en] Given a finite but large set of objects de- scribed by a vector of features, only a small subset of which have been labeled as ‘positive’ with respect to a class of interest, we consider the problem of characterizing the positive class. We formalize this as the problem of learning a feature based score function that minimizes the p-value of a non parametric statistical hypothesis test. For lin- ear score functions over the original feature space or over one of its kernelized versions, we provide a solution of this problem computed by a one-class SVM applied on a surrogate dataset obtained by sampling subsets of the overall set of objects and representing them by their average feature-vector shifted by the average feature-vector of the original sample of positive examples. We carry out experiments with this method on the prediction of targets of transcription factors in two different organisms, E. Coli and S. Cererevisiae. Our method extends enrichment analysis commonly carried out in Bioinformatics and its results outperform common solutions to this problem.
Systems and modeling
Researchers ; Students
http://hdl.handle.net/2268/87877

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