Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Mixture models for learning from incomplete data
Computational learning theory and natural learning systems: Volume IV
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Mining Imperfect Data: Dealing with Contamination and Incomplete Records
Mining Imperfect Data: Dealing with Contamination and Incomplete Records
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An efficient star acquisition method based on SVM with mixtures of kernels
Pattern Recognition Letters
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Incorporating an EM-Approach for Handling Missing Attribute-Values in Decision Tree Induction
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Data Mining and Knowledge Discovery
The problem of disguised missing data
ACM SIGKDD Explorations Newsletter
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
The pairwise attribute noise detection algorithm
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Semi-parametric optimization for missing data imputation
Applied Intelligence
Cleaning disguised missing data: a heuristic approach
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
DiMaC: a system for cleaning disguised missing data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Learning from incomplete data with infinite imputations
Proceedings of the 25th international conference on Machine learning
Class Noise Mitigation Through Instance Weighting
ECML '07 Proceedings of the 18th European conference on Machine Learning
Fuzzy logic supported sketch based image information enhancement
International Journal of Advanced Intelligence Paradigms
POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases
Expert Systems with Applications: An International Journal
NIIA: Nonparametric Iterative Imputation Algorithm
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Data Mining and Knowledge Discovery
Modern Applied Statistics with S
Modern Applied Statistics with S
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
A Novel Framework for Imputation of Missing Values in Databases
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Information enhancement techniques are desired in many areas such as data mining, machine learning, business intelligence, and web data analysis. Information enhancement mainly includes the following topics: data cleaning, data preparation and transformation, missing values imputation, feature and instance selection, feature construction, treatment of noisy and inconsistent data, data integration, data collection and housing, information enhancement, web data availability, web data capture and representation, and the others. It is impossible to outline all the research topics in a single paper. In this study, we discuss the information enhancement for data mining with existing missing data imputation techniques. We first review the current research on imputing missing values, and then experimentally evaluate the techniques and demonstrate the efficiency of missing data imputation techniques to enhance information in the process of pattern discovery from datasets with missing values. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 284–295 DOI: 10.1002/widm.21