Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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 for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization
ECDL '00 Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Scoring and Selecting Terms for Text Categorization
IEEE Intelligent Systems
Some Effective Techniques for Naive Bayes Text Classification
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
An Empirical Study of Category Skew on Feature Selection for Text Categorization
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
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Supervised text categorization is a machine learning task where a predefined category label is automatically assigned to a previously unlabelled document based upon characteristics of the words contained in the document. Since the number of unique words in a learning task (i.e., the number of features) can be very large, the efficiency and accuracy of the learning task can be increased by using feature selection methods to extract from a document a subset of the features that are considered most relevant. In this paper, we introduce a new feature selection method called categorical proportional difference (CPD), a measure of the degree to which a word contributes to differentiating a particular category from other categories. The CPD for a word in a particular category in a text corpus is a ratio that considers the number of documents of a category in which the word occurs and the number of documents from other categories in which the word also occurs. We conducted a series of experiments to evaluate CPD when used in conjunction with SVM and Naive Bayes text classifiers on the OHSUMED, 20 Newsgroups, and Reuters-21578 text corpora. Recall, precision, and the F-measure were used as the measures of performance. The results obtained using CPD were compared to those obtained using six common feature selection methods found in the literature: χ2, information gain, document frequency, mutual information, odds ratio, and simplified χ2. Empirical results showed that, in general, according to the F-measure, CPD outperforms the other feature selection methods in four out of six text categorization tasks.