The nature of statistical learning theory
The nature of statistical learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Introduction to Modern Information Retrieval
Introduction to Modern 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
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Text Classification using Local Latent Semantic Indexing
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
BNS feature scaling: an improved representation over tf-idf for svm text classification
Proceedings of the 17th ACM conference on Information and knowledge management
Projected-prototype based classifier for text categorization
Knowledge-Based Systems
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The increasing availability of digital documents in the last decade has prompted the development of machine learning techniques to automatically classify and organize text documents. The majority of text classification systems rely on the vector space model, which represents the documents as vectors in the term space. Each vector component is assigned a weight that reflects the importance of the term in the document. Typically, these weights are assigned using an information retrieval (IR) approach, such as the famous tf-idf function. In this work, we study two weighting schemes based on information gain and chi-square statistics. These schemes take advantage of the category label information to weight the terms according to their distributions across the different categories. We show that using these supervised weights instead of conventional unsupervised weights can greatly improve the performance of the k-nearest neighbor (KNN) classifier. Experimental evaluations, carried out on multiple text classification tasks, demonstrate the benefits of this approach in creating accurate text classifiers.