C4.5: programs for machine learning
C4.5: programs for machine learning
Overfitting and undercomputing in machine learning
ACM Computing Surveys (CSUR)
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Type Schemes in Databases
DEXA '96 Proceedings of the 7th International Conference on Database and Expert Systems Applications
Learning Concepts From Databases
DEXA '98 Proceedings of the 9th International Conference on Database and Expert Systems Applications
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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We discuss how to post-evaluate inductive classification based on users belief. Although we could learn classification rules inductively by means of decision tree generation, we wonder whether it is consistent with our utilization or not. In this investigation we discuss how to obtain assessment of learning results by verifying belief. Our idea is based on Decision Tree with Hierarchy to class and attributes; to each attribute we assume taxonomy on the domain in addition to class hierarchy. Then, given a firm belief (such as regulation and top executive policy), we check whether the trees satisfy it and we can see the usefulness of the trees.