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
Machine Learning
Machine Learning
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Induction of Classification Rules by Granular Computing
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
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
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
Two-phase rule induction from incomplete data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
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A two-phase learning strategy for rule induction from incomplete data is proposed, and a new form of rules 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 the imputation of 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. Experiments validate the effectiveness of the proposed method.