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Seventh IEEE International Conference on Data Mining (ICDM 2007) (2007)
Omaha, Nebraska, USA
Oct. 28, 2007 to Oct. 31, 2007
ISSN: 1550-4786
ISBN: 0-7695-3018-4
pp: 451-456
ABSTRACT
Feature selection on multi-label documents for automatic text categorization is an under-explored research area. This paper presents a systematic document transformation framework, whereby the multi-label documents are transformed into single-label documents before applying standard feature selection algorithms, to solve the multi-label feature selection problem. Under this framework, we undertake a comparative study on four intuitive document transformation approaches and propose a novel approach called Entropy-based Label Assignment (ELA), which assigns the labels weights to a multi-label document based on label entropy. Three standard feature selection algorithms are utilized for evaluating the document transformation approaches in order to verify its impact on multi-class text categorization problems. Using a SVM classifier and two multi-label evaluation benchmark text collections, we show that the choice of document transformation approaches can significantly influence the performance of multi-class categorization and that our proposed document transformation approach ELA can achieve better performance than all other approaches.
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CITATION

W. Chen, Z. Chen, Q. Yang, B. Zhang and J. Yan, "Document Transformation for Multi-label Feature Selection in Text Categorization," Seventh IEEE International Conference on Data Mining (ICDM 2007)(ICDM), Omaha, Nebraska, USA, 2007, pp. 451-456.
doi:10.1109/ICDM.2007.18
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