Making large-scale support vector machine learning practical
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature selection and feature extraction for text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Imbalanced text classification: A term weighting approach
Expert Systems with Applications: An International Journal
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In this paper we present an iterative voting (IV) method using the density based weighting for text classification. An in-class word density is used to weight for each word in a topic, so that the word in documents has an array of weights to vote for given topics, and the highest scored topic will be labeled. During the voting process, the iteration strategy is applied for improving the classification effectiveness. This method shows the competitive performance against SVM, NB, KNN, and it has better time efficiency.