C4.5: programs for machine learning
C4.5: programs for machine learning
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Induction of Classification Rules by Granular Computing
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Concept Formation and Learning: A Cognitive Informatics Perspective
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Granular Computing: Granular Classifiers and Missing Values
COGINF '07 Proceedings of the 6th IEEE International Conference on Cognitive Informatics
An experimental comparison of three rough set approaches to missing attribute values
Transactions on rough sets VI
A Two-Phase Model for Learning Rules from Incomplete Data
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Research of granularity pair and similar hierarchical algorithm
ICICA'10 Proceedings of the First international conference on Information computing and applications
Mining incomplete data: a rough set approach
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
An interval set model for learning rules from incomplete information table
International Journal of Approximate Reasoning
A Two-Phase Model for Learning Rules from Incomplete Data
Fundamenta Informaticae - Fundamentals of Knowledge Technology
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A framework of learning a new form of rules from incomplete data is introduced so that a user can easily identify attributes with or without missing values in a rule. Two levels of measurement are assigned to a rule. An algorithm for two-phase rule induction is presented. Instead of filling in missing attribute values before or during the process of rule induction, we divide rule induction into two phases. In the first phase, rules and partial rules are induced based on non-missing values. In the second phase, partial rules are modified and refined by filling in some missing values. Such rules truthfully reflect the knowledge embedded in the incomplete data. The study not only presents a new view of rule induction from incomplete data, but also provides a practical solution.