Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A comparison of collocation-based similarity measures in query expansion
Information Processing and Management: an International Journal
Fuzzy Sets and Systems
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Information Processing and Management: an International Journal
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Retrieval Method for Multi-category Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
s-grams: Defining generalized n-grams for information retrieval
Information Processing and Management: an International Journal
Text document clustering based on neighbors
Data & Knowledge Engineering
Recognition of word collocation habits using frequency rank ratio and inter-term intimacy
Expert Systems with Applications: An International Journal
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The pervasiveness of information available on the internet means that increasing numbers of documents must be classified. Text categorization is not only undertaken by domain experts, but also by automatic text categorization systems. Therefore, a text categorization system with a multi-label classifier is necessary to process the large number of documents. In this study, a proposed multi-label text categorization system is developed to classify multi-label documents. Data mapping is performed to transform data from a high-dimensional space to a lower-dimensional space with paired SVM output values, thus lowering the complexity of the computation. A pairwise comparison approach is applied to set the membership function in each predicted class to judge all possible classified classes. To better explain the proposed model, a comparative study using Reuter's data sets is performed on several multi-label approaches such as Naive Bayes, Multi-Label Mixture, Jaccard Kernel and Bp-MLL. Though the comparative results of the empirical experiment indicate that the proposed multi-label text categorization system performs better than other methods in terms of overall performance indices, these comparisons are done under the conditions without knowing original settings of parameters. From these comparative studies, it is found that these probabilities of documents appearing in correctly predicted classes and those of documents appearing in the wrongly predicted classes are important properties and we conclude that the probability of 0.5 for model membership function is a good criterion to judge between correctly and incorrectly classified documents from the results of the empirical experiment.