Statistical analysis with missing data
Statistical analysis with missing data
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Rules in incomplete information systems
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Learning missing values from summary constraints
ACM SIGKDD Explorations Newsletter
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Machine Learning
Modeling and Imputation of Large Incomplete Multidimensional Datasets
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
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
Association Rules in Incomplete Databases
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
A Closest Fit Approach to Missing Attribute VAlues in Preterm Birth Data
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
On decomposition for incomplete data
Fundamenta Informaticae
OLAP over uncertain and imprecise data
The VLDB Journal — The International Journal on Very Large Data Bases
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
DS '08 Proceedings of the 11th International Conference on Discovery Science
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
On the quest for optimal rule learning heuristics
Machine Learning
Rough sets handling missing values probabilistically interpreted
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Dealing with missing data: algorithms based on fuzzy set and rough set theories
Transactions on Rough Sets IV
Characteristic relations for incomplete data: a generalization of the indiscernibility relation
Transactions on Rough Sets IV
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In this paper, we review possible strategies for handling missing values in separate-and-conquer rule learning algorithms, and compare them experimentally on a large number of datasets. In particular through a careful study with data with controlled levels of missing values we get additional insights on the strategies' different biases w.r.t. attributes with missing values. Somewhat surprisingly, a strategy that implements a strong bias against the use of attributes with missing values, exhibits the best average performance on 24 datasets from the UCI repository.