Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Cost Sensitive Discretization of Numeric Attributes
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Application of rough sets in the presumptive diagnosis of urinary system diseases
Artificial intelligence and security in computing systems
Knowledge Representation Techniques (Studies in Fuzziness and Soft Computing)
Knowledge Representation Techniques (Studies in Fuzziness and Soft Computing)
Fuzzy and Rough Techniques in Medical Diagnosis and Medication (Studies in Fuzziness and Soft Computing)
Cluster Analysis
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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This article presents the LDGen method which is based on genetic algorithm. The author proposed evolutionary approach to the solution of the discretization problem for systems that induce rules on the basis of rough sets theory. The study describes details of the method with special focus on the crossing operator. The proposed approach concerns working with multidimensional samples. Thanks to application of the author's own method of for visualizing multidimensionality, i.e. so called Pipes of Samples, it was possible to visualize up to 360 dimensions, which is usually sufficient in case of problems the Rough Sets Theory deals with. Mutation and crossing methods were developed using this visualisation so that, for real numbers, it allowed to create individuals that describe one solution of the discretization. Hence the population is a set of many complete discretizations of all the attributes.