Prime Implicants, Minimum Covers, and the Complexity of Logic Simplification
IEEE Transactions on Computers
International Journal of Man-Machine Studies
Absolute Minimization of Completely Specified Switching Functions
IEEE Transactions on Computers
A comparison of the decision table and tree
Communications of the ACM
C4.5: programs for machine learning
C4.5: programs for machine learning
The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
Logic synthesis
Average case analysis of k-CNF and k-DNF learning algorithms
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
From decision tables to expert system shells
Data & Knowledge Engineering
Two-level logic minimization: an overview
Integration, the VLSI Journal
Predicting equity returns from securities data
Advances in knowledge discovery and data mining
Conversion of decision tables to efficient sequential testing procedures
Communications of the ACM
Optimal conversion of extended-entry decision tables with general cost criteria
Communications of the ACM
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Machine Learning
IEEE Expert: Intelligent Systems and Their Applications
Knowledge discovery with second-order relations
Knowledge and Information Systems
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Test-driven specification: paradigm and automation
Proceedings of the 44th annual Southeast regional conference
An optimization of ReliefF for classification in large datasets
Data & Knowledge Engineering
Rough sets based association rules application for knowledge-based system design
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Towards directly applied ontological constraints in a semantic decision table
RuleML'11 Proceedings of the 5th international conference on Rule-based modeling and computing on the semantic web
Using SOIQ(D) to formalize semantics within a semantic decision table
RuleML'12 Proceedings of the 6th international conference on Rules on the Web: research and applications
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Decision tables are widely used in many knowledge-based and decision support systems. They allow relatively complex logical relationships to be represented in an easily understood form and processed efficiently. This paper describes second-order decision tables (decision tables that contain rows whose components have sets of atomic values) and their role in knowledge engineering to: (1) support efficient management and enhance comprehensibility of tabular knowledge acquired by knowledge engineers, and (2) automatically generate knowledge from a tabular set of examples. We show how second-order decision tables can be used to restructure acquired tabular knowledge into a condensed but logically equivalent second-order table. We then present the results of experiments with such restructuring. Next, we describe SORCER, a learning system that induces second-order decision tables from a given database. We compare SORCER with IDTM, a system that induces standard decision tables, and a state-of-the-art decision tree learner, C4.5. Results show that in spite of its simple induction methods, on the average over the data sets studied, SORCER has the lowest error rate.