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Learning from positive and unlabeled examples by enforcing statistical significance
Geurts, Pierre
2011In Proceedings of Machine Learning Research, 15
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Keywords :
machine learning; kernel methods; semi-supervised learning; bioinformatics; gene regulatory networks
Abstract :
[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.
Research center :
Systems and modeling
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Learning from positive and unlabeled examples by enforcing statistical significance
Publication date :
April 2011
Event name :
Fourteenth International Conference on Artificial Intelligence and Statistics
Event organizer :
Geoffrey Gordon, David Dunson, and Miroslav Dudík
Event place :
Miami, United States
Event date :
April 11-13
Audience :
International
Journal title :
Proceedings of Machine Learning Research
eISSN :
2640-3498
Publisher :
Microtome Publishing, Brookline, United States - Massachusetts
Special issue title :
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics
Volume :
15
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 28 March 2011

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