Statistical analysis with missing data
Statistical analysis with missing data
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Locally linear reconstruction for instance-based learning
Pattern Recognition
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
Combination of KNN-Based Feature Selection and KNNBased Missing-Value Imputation of Microarray Data
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Using Imputation Techniques to Help Learn Accurate Classifiers
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Missing data imputation: a fuzzy K-means clustering algorithm over sliding window
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Missing value imputation based on data clustering
Transactions on computational science I
Imputation of missing values for compositional data using classical and robust methods
Computational Statistics & Data Analysis
Artificial Intelligence in Medicine
Missing Value Estimation for Mixed-Attribute Data Sets
IEEE Transactions on Knowledge and Data Engineering
Incorporating Nonlinear Relationships in Microarray Missing Value Imputation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Using classifier-based nominal imputation to improve machine learning
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
A Novel Framework for Imputation of Missing Values in Databases
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
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Most learning algorithms generally assume that data is complete so each attribute of all instances is filled with a valid value. However, missing values are very common in real datasets for various reasons. In this paper, we propose a new single imputation method based on locally linear reconstruction (LLR) that improves the prediction performance of supervised learning (classification & regression) with missing values. First, we investigate how missing values degrade the prediction performance with various missing ratios. Next, we compare the proposed missing value imputation method (LLR) with six well-known single imputation methods for five different learning algorithms based on 13 classification and nine regression datasets. The experimental results showed that (1) all imputation methods helped to improve the prediction accuracy, although some were very simple; (2) the proposed LLR imputation method enhanced the modeling performance more than all other imputation methods, irrespective of the learning algorithms and the missing ratios; and (3) LLR was outstanding when the missing ratio was relatively high and its prediction accuracy was similar to that of the complete dataset.