Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Handbook of data mining and knowledge discovery
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning rules with negation for text categorization
Proceedings of the 2007 ACM symposium on Applied computing
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
A Web page classification system based on a genetic algorithm using tagged-terms as features
Expert Systems with Applications: An International Journal
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This paper presents a Genetic Algorithm, called Olex-GA, for the induction of rule-based text classifiers of the form "classify document dunder category cif t1ï戮驴 dor ... or tnï戮驴 dand not (tn+ 1ï戮驴 dor ... or tn+ mï戮驴 d) holds", where each tiis a term. Olex-GA relies on an efficient several-rules-per-individualbinary representation and uses the F-measure as the fitness function. The proposed approach is tested over the standard test sets Reuters-21578and Ohsumedand compared against several classification algorithms (namely, Naive Bayes, Ripper, C4.5, SVM). Experimental results demonstrate that it achieves very good performance on both data collections, showing to be competitive with (and indeed outperforming in some cases) the evaluated classifiers.