Transductive relational classification in the co-training paradigm

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DOIResolve DOI: http://doi.org/10.1007/978-3-642-31537-4_2
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TypeBook Chapter
Proceedings titleMachine Learning and Data Mining in Pattern Recognition : 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings
Series titleLecture Notes In Computer Science; Volume 7376
Conference8th International Conference on Machine Learning and Data Mining (MLDM) 2012, July 13-20, 2012 Berlin, Germany
ISSN0302-9743
ISBN978-3-642-31536-7
978-3-642-31537-4
Pages1125; # of pages: 15
Subjecttransductive learning; co-training; multi-relational classification
AbstractConsider a multi-relational database, to be used for classification, that contains a large number of unlabeled data. It follows that the cost of labeling such data is prohibitive. Transductive learning, which learns from labeled as well as from unlabeled data already known at learning time, is highly suited to address this scenario. In this paper, we construct multi-views from a relational database, by considering different subsets of the tables as contained in a multi-relational database. These views are used to boost the classification of examples in a co-training schema. The automatically generated views allow us to overcome the independence problem that negatively affect the performance of co-training methods. Our experimental evaluation empirically shows that co-training is beneficial in the transductive learning setting when mining multi-relational data and that our approach works well with only a small amount of labeled data.
Publication date
PublisherSpringer Berlin Heidelberg
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number20494948
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Record identifier31180664-b569-4563-b2d9-d685d276240a
Record created2012-08-15
Record modified2016-07-19
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