Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
IEEE Expert: Intelligent Systems and Their Applications
ECML '93 Proceedings of the European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European 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 associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Associative text categorization exploiting negated words
Proceedings of the 2006 ACM symposium on Applied computing
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
Learning rules with negation for text categorization
Proceedings of the 2007 ACM symposium on Applied computing
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This paper reports on an investigation to compare a number of strategies to include negated features within the process of Inductive Rule Learning (IRL). The emphasis is on generating the negation of features while rules are being "learnt"; rather than including (or deriving) the negation of all features as part of the input. Eight different strategies are considered based on the manipulation of three feature sub-spaces. Comparisons are also made with Associative Rule Learning (ARL) in the context of multi-class text classification. The results indicate that the option to include negated features within the IRL process produces more effective classifiers.