A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
Centroid-Based Document Classification: Analysis and Experimental Results
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Knowledge and Information Systems
Fragments and text categorization
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Exploiting the systematic review protocol for classification of medical abstracts
Artificial Intelligence in Medicine
Building systematic reviews using automatic text classification techniques
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
| Hi-index | 0.00 |
Good feature selection is essential for text classification to make it tractable for machine learning, and to improve classification performance. This study benchmarks the performance of twelve feature selection metrics across 229 text classification problems drawn from Reuters, OHSUMED, TREC, etc. using Support Vector Machines. The results are analyzed for various objectives. For best accuracy, F-measure or recall, the findings reveal an outstanding new feature selection metric, "Bi-Normal Separation" (BNS). For precision alone, however, Information Gain (IG) was superior. A new evaluation methodology is offered that focuses on the needs of the data mining practitioner who seeks to choose one or two metrics to try that are mostly likely to have the best performance for the single dataset at hand. This analysis determined, for example, that IG and Chi-Squared have correlated failures for precision, and that IG paired with BNS is a better choice.