Statistical analysis with missing data
Statistical analysis with missing data
Proceedings of the seventh international conference (1990) on Machine learning
Incremental Learning of Bayesian Networks with Hidden Variables
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Algorithmic Description of ACS2
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Incomplete Database Issues for Representative Association Rules
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Using Decision Tree Induction for Discovering Holes in Data
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Spectral estimation under nature missing data
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Mining breast cancer data with XCS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Missing data pose a potential threat to learning and classification in that they may compromise the ability of a system to develop robust, generalized models of the environment in which they operate. This investigation reports on the effects of three approaches to covering these data using an XCS-style learning classifier system. Using fabricated datasets representing a wide range of missing value densities, it was found that missing data do not appear to adversely affect LCS learning and classification performance. Furthermore, three types of missing value covering were found to exhibit similar efficiency on these data, with respect to convergence rate and classification accuracy.