Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
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
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Feature Subset Selection in Text-Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
A comparison of text-categorization methods applied to n-gram frequency statistics
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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In this paper we propose PARTfs which adopts a supervised machine learning algorithm, namely partial decision trees, as a method for feature subset selection. In particular, it is shown that an aggressive reduction of the feature space can be achieved with PARTfs while still allowing for comparable classification results with conventional feature selection metrics. The approach is empirically verified by employing two different document representations and four different text classification algorithms that are applied to a document collection consisting of personal e-mail messages. The results show that a reduction of the feature space in the magnitude of ten is achievable without loss of classification accuracy.