Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
Text databases & document management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
IEEE Expert: Intelligent Systems and Their Applications
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Associative text categorization exploiting negated words
Proceedings of the 2006 ACM symposium on Applied computing
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
The class imbalance problem: A systematic study
Intelligent Data Analysis
A decision-tree-based symbolic rule induction system for text categorization
IBM Systems Journal
TRIPPER: rule learning using taxonomies
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A Genetic Algorithm for Text Classification Rule Induction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A 25-year perspective on logic programming
Classification inductive rule learning with negated features
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
The disjunctive datalog system DLV
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
The intelligent grounder of DLV
Correct Reasoning
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This paper describes Olex, a novel method for the automatic construction of rule-based text classifiers. Olex relies on an optimization algorithm whereby a set of (both positive and negative) discriminating terms is generated for the category being learned. Such terms are then used to construct a classifier of the form "if term t1 or ... term tn occurs in document d, and none of terms tn--1, · · · tn--m occurs in d, then d belongs to category c". The proposed method is simple and elegant. Despite this, the results of a systematic experimentation performed on both the REUTERS-21578 and the OHSUMED data collections show that Olex is both effective and efficient.