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Return to Categorization and supervised machine learning
Many classification problems require classifiers to assign each single document into more than one category, which is called multi-labelled classification. The categories in such problems usually are neither conditionally independent from each other nor mutually exclusive, therefore it is not trivial to directly employ state-of-the-art classification algorithms without losing information of relation among categories. In this paper, we explore correlations among categories with maximum entropy method and derive a classification algorithm for multi-labelled documents. Our experiments show that this method significantly outperforms the combination of single label approach.
@inproceedings{1076082, author = {Shenghuo Zhu and Xiang Ji and Wei Xu and Yihong Gong}, title = {Multi-labelled classification using maximum entropy method}, booktitle = {SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval}, year = {2005}, isbn = {1-59593-034-5}, pages = {274--281}, location = {Salvador, Brazil}, doi = {http://doi.acm.org/10.1145/1076034.1076082}, publisher = {ACM Press}, address = {New York, NY, USA}, }  ©2006 Association for Computing Machinery |
