Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Progress in Intelligent Data Analysis
Applied Intelligence
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Machine Learning
A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction
Applied Intelligence
Adapting k-means for supervised clustering
Applied Intelligence
An instance-based learning approach based on grey relational structure
Applied Intelligence
Imputation through finite Gaussian mixture models
Computational Statistics & Data Analysis
Explaining inferences in Bayesian networks
Applied Intelligence
POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases
Expert Systems with Applications: An International Journal
Missing Data Analysis: A Kernel-Based Multi-Imputation Approach
Transactions on Computational Science III
AN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREES
Applied Artificial Intelligence
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
New imputation methods for missing data using quantiles
Journal of Computational and Applied Mathematics
Missing value imputation based on data clustering
Transactions on computational science I
A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data
Expert Systems with Applications: An International Journal
Efficient Markov chain Monte Carlo with incomplete multinomial data
Statistics and Computing
Missing data imputation by utilizing information within incomplete instances
Journal of Systems and Software
Missing data analysis with fuzzy C-Means: A study of its application in a psychological scenario
Expert Systems with Applications: An International Journal
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
Missing data imputation in multivariate data by evolutionary algorithms
Computers in Human Behavior
Applied Intelligence
A robust missing value imputation method for noisy data
Applied Intelligence
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A Bayesian imputation method for a clustering genetic algorithm
Journal of Computational Methods in Sciences and Engineering
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Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions.This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST).Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.