C4.5: programs for machine learning
C4.5: programs for machine learning
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Overfitting and undercomputing in machine learning
ACM Computing Surveys (CSUR)
A database perspective on knowledge discovery
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Machine Learning
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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 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
KDEX '97 Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop
Knowledge Acquisition for Classification Systems
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
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In this investigation we discuss how to improve the quality of decision trees, one of the classification techniques in compensation for small loss of amount of information. To do that, we assume a semantic hierarchy among classes which is ignored in conventional stories. The basic idea comes from relaxing class membership by using the hierarchy and we explore how to preserve the precision of classification in a sense of entropy.