Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Using random walks for multi-label classification
Proceedings of the 20th ACM international conference on Information and knowledge management
Classifying websites into non-topical categories
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Feature selection for multi-label classification using multivariate mutual information
Pattern Recognition Letters
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach
Electronic Notes in Theoretical Computer Science (ENTCS)
Probabilistic multi-label classification with sparse feature learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.