Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
A new version of the rule induction system LERS
Fundamenta Informaticae
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
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
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
On decomposition for incomplete data
Fundamenta Informaticae
Rough sets handling missing values probabilistically interpreted
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Extended rough set-based attribute reduction in inconsistent incomplete decision systems
Information Sciences: an International Journal
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
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One of the newest approaches to missing attribute values in data sets is based on a valued tolerance relation. The valued tolerance relation method of handling missing attribute values was not yet experimentally compared with other methods. The main objective of this paper was to compare the quality of two methods handling missing attribute values, one of them was the valued tolerance method, the other method was the MLEM2 approach, using the same interpretation of missing attribute values but a different approach to computing approximations and rule induction. Both methods were compared using not only an error rate, a result of ten-fold cross validation, but also complexity of induced rule sets. Our conclusion is that neither of these two methods is better in terms of the error rate. However, the MLEM2 approach produces, in most cases, less complex rule sets than the valued tolerance method.