Boolean Feature Discovery in Empirical Learning
Machine Learning
The induction of probabilistic rule sets—the Itrule algorithm
Proceedings of the sixth international workshop on Machine learning
The Utility of Knowledge in Inductive Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Machine Learning
Global Data Analysis and the Fragmentation Problem in Decision Tree Induction
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Generating Models of Mental Retardation from Data with Machine Learning
KDEX '97 Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Performance of classification models from a user perspective
Decision Support Systems
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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We introduce a new bias for rule learning systems. The bias only allows a rule learner to create a rule that predicts class membership if each test of the rule in isolation is predictive of that class. Although the primary motivation for the bias is to improve the understandability of rules, we show that it also improves the accuracy of learned models on a number of problems. We also introduce a related preference bias that allows creating rules that violate this restriction if they are statistically significantly better than alternative rules without such violations.