| Download | - View accepted manuscript: Iterative classification for multiple target attributes (PDF, 733 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1007/s10844-012-0224-5 |
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| Author | Search for: Guo, Hongyu1; Search for: Létourneau, Sylvain1 |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Subject | multi-target learning; multitask learning; iterative classification; data mining |
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| Abstract | Many 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. |
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| Publication date | 2013-04-01 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21262546 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | ff6b32a6-a0ca-45bf-ad71-ba54d7acd766 |
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| Record created | 2013-03-13 |
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| Record modified | 2020-06-04 |
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