Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Machine learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine Learning
Attribute Dependencies, Understandability and Split Selection in Tree Based Models
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Ordered Estimation of Missing Values
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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We describe an approach to fill missing values in decision trees during classification. This approach is derived from the ordered attribute trees method, proposed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Both our approach and theirs are based on the Mutual Information between the attributes and the class. Our method takes the dependence between attributes into account by using the Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we present tests performed on some real databases using our approach and Quinlan's method. We analyse the classification results of some instances in test data and finally we discuss some perspectives.