Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
On applying linear discriminant analysis for multi-labeled problems
Pattern Recognition Letters
On Applying Dimension Reduction for Multi-labeled Problems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
MCut: a thresholding strategy for multi-label classification
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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In the world of text document classification, the most general case is that in which a document can be classified into more than one category, the multi-label problem. This paper investigates the performance of two document classification systems applied to the task of multi-class multi-label document classification. Both systems consider the pattern of co-occurrences in documents of multiple categories. One system is based on a novel sequential data representation combined with a kNN classifier designed to make use of sequence information. The other is based on the “Latent Semantic Indexing” analysis combined with the traditional kNN classifier. The experimental results show that the first system performs better than the second on multi-labeled documents, while the second performs better on uni-labeled documents. Performance therefore depends on the dataset applied and the objective of the application.