Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
RoboCup 2001: Robot Soccer World Cup V
RoboCup 2001: Robot Soccer World Cup V
ICML '00 Proceedings of the Seventeenth International Conference on 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
A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Cluster-Based Algorithms for Dealing with Missing Values
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Application of Data Decomposition to Incomplete Information Systems
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Evaluation of the decision performance of the decision rule set from an ordered decision table
Knowledge-Based Systems
Hi-index | 0.00 |
In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. This method can be applied to any algorithm of classifier induction. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to these sets. Finally, a conflict resolving method is used to obtain final classification from partial classifiers. We provide an empirical evaluation of the decomposition method accuracy and model size with use of various decomposition criteria on data with natural missing values. We present also experiments on data with synthetic missing values to examine the properties of proposed method with variable ratio of incompleteness.