Iterative classification for multiple target attributes

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Journal titleJournal of Intelligent Information Systems
Pages283305; # of pages: 23
Subjectmulti-target learning ; multitask learning ; iterative classification ; data mining
AbstractMany real-world applications require the simultaneous prediction of multiple target attributes. The techniques currently available for these problems either employ a global model that simultaneously predicts all target attributes or rely on the aggregation of individual models, each predicting one target. This paper introduces a novel solution. Our approach employs an iterative classification strategy to exploit the relationships among multiple target attributes to achieve higher accuracy. The computation scheme is developed as a wrapper in which many standard single-target classification algorithms can be simply “plugged-in” to simultaneously predict multiple targets. An empirical evaluation using eight data sets shows that the proposed method outperforms 1) an approach that constructs independent classifiers for each target, 2) a multitask neural network method, and 3) ensembles of multi-objective decision trees in terms of simultaneously predicting all target attributes correctly.
Publication date
PublisherSpringer US
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21262546
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Record identifierff6b32a6-a0ca-45bf-ad71-ba54d7acd766
Record created2013-03-13
Record modified2016-05-09
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