C4.5: programs for machine learning
C4.5: programs for machine learning
Induction and polynomial networks
Network models for control and processing
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Inductive Learning Algorithms for Complex Systems Modeling
Inductive Learning Algorithms for Complex Systems Modeling
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Clustering incomplete relational data using the non-Euclidean relational fuzzy c-means algorithm
Pattern Recognition Letters
Systems Analysis Modelling Simulation
A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction
Applied Intelligence
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Building Bayesian Network Models in Medicine: The MENTOR Experience
Applied Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
GMDH-based feature ranking and selection for improved classification of medical data
Journal of Biomedical Informatics
On Classification with Incomplete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-parametric optimization for missing data imputation
Applied Intelligence
Engineering Applications of Artificial Intelligence
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
Bayesian networks for imputation in classification problems
Journal of Intelligent Information Systems
A comprehensive empirical evaluation of missing value imputation in noisy software measurement data
Journal of Systems and Software
A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values
Expert Systems with Applications: An International Journal
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
Classification algorithm sensitivity to training data with non representative attribute noise
Decision Support Systems
RETRACTED: Investigating the efficiency in oil futures market based on GMDH approach
Expert Systems with Applications: An International Journal
A Conservative Feature Subset Selection Algorithm with Missing Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
AN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREES
Applied Artificial Intelligence
A Novel Framework for Imputation of Missing Values in Databases
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mining With Noise Knowledge: Error-Aware Data Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
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Missing data imputation is an important research topic in data mining. The impact of noise is seldom considered in previous works while real-world data often contain much noise. In this paper, we systematically investigate the impact of noise on imputation methods and propose a new imputation approach by introducing the mechanism of Group Method of Data Handling (GMDH) to deal with incomplete data with noise. The performance of four commonly used imputation methods is compared with ours, called RIBG (robust imputation based on GMDH), on nine benchmark datasets. The experimental result demonstrates that noise has a great impact on the effectiveness of imputation techniques and our method RIBG is more robust to noise than the other four imputation methods used as benchmark.