The Strength of Weak Learnability
Machine Learning
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
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Stochastic Attribute Selection Committees
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Inductive learning models with missing values
Mathematical and Computer Modelling: An International Journal
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Handling missing attribute values is an important issue for classifier learning, since missing attribute values in either training data or test (unseen) data affect the prediction accuracy of learned classifiers. In many real KDD applications, attributes with missing values are very common. This paper studies the robustness of four recently developed committee learning techniques, including Boosting, Bagging, Sasc, and SascMB, relative to C4.5 for tolerating missing values in test data. Boosting is found to have a similar level of robustness to C4.5 for tolerating missing values in test data in terms of average error in a representative collection of natural domains under investigation. Bagging performs slightly better than Boosting, while Sasc and SascMB perform better than them in this regard, with SascMB performing best.