Neural network design
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Clustering analysis of competitive learning network for molecular data
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Self-Organizing map clustering analysis for molecular data
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
An improved method of feature selection based on concept attributes in text classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
The Chinese text categorization system with association rule and category priority
Expert Systems with Applications: An International Journal
Improving the performance of association classifiers by rule prioritization
Knowledge-Based Systems
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The feature selection is an important part in automatic text classification. In this paper, we use a Chinese semantic dictionary -- Hownet to extract the concepts from the word as the feature set, because it can better reflect the meaning of the text. We construct a combined feature set that consists of both sememes and the Chinese words, propose a CHI-MCOR weighing method according to the weighing theories and classification precision. The effectiveness of the competitive network and the Radial Basis Function (RBF) network in text classification are examined. Experimental result shows that if the words are extracted properly, not only the feature dimension is smaller but also the classification precision is higher, the RBF network outperform competitive network for automatic text classification because of the application of supervised learning. Besides its much shorter training time than the BP network's, the RBF network makes precision and recall rates that are almost at the same level as the BP network's.