Information Processing Letters
Cause-effect relationships and partially defined Boolean functions
Annals of Operations Research
C4.5: programs for machine learning
C4.5: programs for machine learning
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
Decomposability of partially defined Boolean functions
Discrete Applied Mathematics - Special volume on partitioning and decomposition in combinatorial optimization
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
Complexity of identification and dualization of positive Boolean functions
Information and Computation
Unifying instance-based and rule-based induction
Machine Learning
On the complexity of dualization of monotone disjunctive normal forms
Journal of Algorithms
Logical analysis of numerical data
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Error-free and best-fit extensions of partially defined Boolean functions
Information and Computation
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Convexity and logical analysis of data
Theoretical Computer Science
New results on monotone dualization and generating hypergraph transversals
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
A Quantitative Study of Small Disjuncts
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Further experimental evidence against the utility of Occam's razor
Journal of Artificial Intelligence Research
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Logical analysis of binary data with missing bits
Artificial Intelligence
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Patterns are the key building blocks in the logical analysis of data (LAD). It has been observed in empirical studies and practical applications that some patterns are more ''suitable'' than others for use in LAD. In this paper, we model various such suitability criteria as partial preorders defined on the set of patterns. We introduce three such preferences, and describe patterns which are Pareto-optimal with respect to any one of them, or to certain combinations of them. We develop polynomial time algorithms for recognizing Pareto-optimal patterns, as well as for transforming an arbitrary pattern to a better Pareto-optimal one with respect to any one of the considered criteria, or their combinations. We obtain analytical representations characterizing some of the sets of Pareto-optimal patterns, and investigate the computational complexity of generating all Pareto-optimal patterns. The empirical evaluation of the relative merits of various types of Pareto-optimality is carried out by comparing the classification accuracy of Pareto-optimal theories on several real life data sets. This evaluation indicates the advantages of ''strong patterns'', i.e. those patterns which are Pareto-optimal with respect to the ''evidential preference'' introduced in this paper.