Cause-effect relationships and partially defined Boolean functions
Annals of Operations Research
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
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
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Dualization, decision lists and identification of monotone discrete functions
Annals of Mathematics and Artificial Intelligence
Decision trees for ordinal classification
Intelligent Data Analysis
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
Reduction of attributes in ordinal decision systems
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Fuzzy ILP Classification of web reports after linguistic text mining
Information Processing and Management: an International Journal
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The classical theory of Rough Sets describes objects by discrete attributes, and does not take into account the ordering of the attributes values. This paper proposes a modification of the Rough Set approach applicable to monotone datasets. We introduce respectively the concepts of monotone discernibility matrix and monotone (object) reduct. Furthermore, we use the theory of monotone discrete functions developed earlier by the first author to represent and to compute decision rules. In particular we use monotone extensions, decision lists and dualization to compute classification rules that cover the whole input space. The theory is applied to the bankruptcy problem.