Rules in incomplete information systems
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Classification of dynamics in rough sets
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Sensitivity and specificity for mining data with increased incompleteness
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Attribute dynamics in rough sets
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Temporal Dynamics in Information Tables
Fundamenta Informaticae - From Physics to Computer Science: to Gianpiero Cattaneo for his 70th birthday
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Our main objective was to verify the following hypothesis: for some complete (i.e., without missing attribute vales) data sets it is possible to induce better rule sets (in terms of an error rate) by increasing incompleteness (i.e., removing some existing attribute values) of the original data sets. In this paper we present detailed results of experiments on one data set, showing that some rule sets induced from incomplete data sets are significantly better than the rule set induced from the original data set, with the significance level of 5%, two-tailed test. Additionally, we discuss criteria for inducing better rules by increasing incompleteness and present graphs for some well-known data sets.