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
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
Flexible Indiscernibility Relations for Missing Attribute Values
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Flexible Indiscernibility Relations for Missing Attribute Values
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
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In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classification. We provide an empirical evaluation of the decomposition method with use of various decomposition criteria.