Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
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
Missing Value Estimation Based on Dynamic Attribute Selection
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
DTU: A Decision Tree for Uncertain Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Evaluation of a probabilistic approach to classify incomplete objects using decision trees
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
| Hi-index | 0.00 |
When attempting to discover by learning concepts embedded in data, it is not uncommon to find that information is missing from the data. Such missing information can diminish the confidence on the concepts learned from the data. This paper describes a new approach to fill missing values in examples provided to a learning algorithm. A decision tree is constructed to determine the missing values of each attribute by using the information contained in other attributes. Also, an ordering for the construction of the decision trees for the attributes is formulated. Experimental results on three datasets show that completing the data by using decision trees leads to final concepts with less error under different rates of random missing values. The approach should be suitable for domains with strong relations among the attributes, and for which improving accuracy is desirable even if computational cost increases.