Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Machine Learning
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Data preparation for data mining
Data preparation for data mining
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
IEEE Expert: Intelligent Systems and Their Applications
Maximizing Text-Mining Performance
IEEE Intelligent Systems
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Dealing with missing data: algorithms based on fuzzy set and rough set theories
Transactions on Rough Sets IV
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A method is presented to induce decision rules from data with missing values where (a) the format of the rules is no different than rules for data without missing values and (b) no special features are specified to prepare the the original data or to apply the induced rules. This method generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal number of unweighted rules. A new example is classified by applying all rules and assigning the example to the class with the most satisfied rules. Disjuncts in rules are naturally overlapping. When combined with voted solutions, the inherent redundancy is enhanced. We provide experimental evidence that this transparent approach to classification can yield strong results for data mining with missing values.