Communications of the ACM
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
An introduction to computational learning theory
An introduction to computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Consistency-based search in feature selection
Artificial Intelligence
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Speculative Markov Blanket Discovery for Optimal Feature Selection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Feature selection for MAUC-oriented classification systems
Neurocomputing
Selecting feature subset via constraint association rules
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
A novel feature subset selection algorithm based on association rule mining
Intelligent Data Analysis
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The evolving and adapting capabilities of robust intelligence are best manifested in its ability to learn. Machine learning enables computer systems to learn, and improve performance. Feature selection facilitates machine learning (e.g., classification) by aiming to remove irrelevant features. Feature (attribute) interaction presents a challenge to feature subset selection for classification. This is because a feature by itself might have little correlation with the target concept, but when it is combined with some other features, they can be strongly correlated with the target concept. Thus, the unintentional removal of these features may result in poor classification performance. It is computationally intractable to handle feature interactions in general. However, the presence of feature interaction in a wide range of real-world applications demands practical solutions that can reduce high-dimensional data while preserving feature interactions. In this paper, we take up the challenge to design a special data structure for feature quality evaluation, and to employ an information-theoretic feature ranking mechanism to efficiently handle feature interaction in subset selection. We conduct experiments to evaluate our approach by comparing with some representative methods, perform a lesion study to examine the critical components of the proposed algorithm to gain insights, and investigate related issues such as data structure, ranking, time complexity, and scalability in search of interacting features.