Communications of the ACM
Inductive knowledge acquisition: a case study
Proceedings of the Second Australian Conference on Applications of expert systems
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Empirical Learning as a Function of Concept Character
Machine Learning
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
A Statistical-Heuristic Feature Selection Criterion for Decision Tree Induction
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
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Inductive machine learning has become an important approach to automated knowledge acquisition from databases. The disjunctive normal form (DNF), as the common analytic representation of decision trees and decision tables (rules), provides a basis for formal analysis of uncertainty and complexity in inductive learning. In this paper, a theory for general decision trees is developed based on Shannon驴s expansion of the discrete DNF, and a probabilistic induction system PIK is further developed for extracting knowledge from real-world data. Then we combine formal and practical approaches to study how data characteristics affect the uncertainty and complexity in inductive learning. Three important data characteristics, namely, disjunctiveness, noise and incompleteness, are studied. The combination of leveled-pruning, leveled-condensing and resampling-estimation turns out to be a very powerful method for dealing with highly-disjunctive and inadequate data. Finally the PIK system is compared with other recent inductive learning systems on a number of real-world domains.