Communications of the ACM - Special issue on parallelism
Introduction to Grey system theory
The Journal of Grey System
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Missing values and learning of fuzzy rules
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An iterative refinement approach for data cleaning
Intelligent Data Analysis
Default clustering with conceptual structures
Journal on data semantics VIII
A robust missing value imputation method for noisy data
Applied Intelligence
Default clustering from sparse data sets
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Nearest neighbor selection for iteratively kNN imputation
Journal of Systems and Software
Data stream classification with artificial endocrine system
Applied Intelligence
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
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This paper proposes a grey-based nearest neighbor approach to predict accurately missing attribute values. First, grey relational analysis is employed to determine the nearest neighbors of an instance with missing attribute values. Accordingly, the known attribute values derived from these nearest neighbors are used to infer those missing values. Two datasets were used to demonstrate the performance of the proposed method. Experimental results show that our method outperforms both multiple imputation and mean substitution. Moreover, the proposed method was evaluated using five classification problems with incomplete data. Experimental results indicate that the accuracy of classification is maintained or even increased when the proposed method is applied for missing attribute value prediction.