Statistical analysis with missing data
Statistical analysis with missing data
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Uncertainly measures of rough set prediction
Artificial Intelligence
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Benchmarking k-nearest neighbour imputation with homogeneous Likert data
Empirical Software Engineering
Flexible Indiscernibility Relations for Missing Attribute Values
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
An iterative refinement approach for data cleaning
Intelligent Data Analysis
Handling incomplete data using evolution of imputation methods
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Using association rules for better treatment of missing values
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
An interval set model for learning rules from incomplete information table
International Journal of Approximate Reasoning
Missing template decomposition method and its implementation in rough set exploration system
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Uncertainty handling in tabular-based requirements using rough sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Flexible Indiscernibility Relations for Missing Attribute Values
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Journal of Intelligent Manufacturing
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We present an algorithm to impute missingvalues from given dataalone, and analyse its performance. Theproposed procedure is based onnon-numeric rule based data analysis, and aimsto maximise consistency of imputation from known values. Incontrast to the prevailingstatistical imputation algorithms, it does notmake representationalassumptions or presupposes other modelconstraints. Therefore, it is suitablefor a wide variety of data – sets, and can beused as a pre-processing step beforeresorting to harder numerical methods.