Text classification based on partial least square analysis
Proceedings of the 2007 ACM symposium on Applied computing
Enhancing portability with multilingual ontology-based knowledge management
Decision Support Systems
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
Can feature information interaction help for information fusion in multimedia problems?
Multimedia Tools and Applications
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
Boosting KNN text classification accuracy by using supervised term weighting schemes
Proceedings of the 18th ACM conference on Information and knowledge management
Putting things in context: a topological approach to mapping contexts to ontologies
Journal on data semantics IX
Text classification with the support of pruned dependency patterns
Pattern Recognition Letters
Using SVM based method for equipment fault detection in a thermal power plant
Computers in Industry
Identification of trends from patents using self-organizing maps
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Latent Semantic Indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called "Local Relevancy Weighted LSI" to improve text classification by performing a separate Single Value Decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension.