Machine Learning - Special issue on learning with probabilistic representations
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
A Bayesian network classifier that combines a finite mixture model and a naïve bayes model
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Nomograms for visualizing support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Detecting statistical interactions with additive groves of trees
Proceedings of the 25th international conference on Machine learning
Fitness Function Comparison for GA-Based Feature Construction
Current Topics in Artificial Intelligence
Mining non-redundant high order correlations in binary data
Proceedings of the VLDB Endowment
Genome-wide efficient attribute selection for purely epistatic models via Shannon entropy
International Journal of Business Intelligence and Data Mining
Searching for interacting features in subset selection
Intelligent Data Analysis
Evolutionary multi-feature construction for data reduction: A case study
Applied Soft Computing
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Feature selection in an electric billing database considering attribute inter-dependencies
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Discovery of regularities in the use of herbs in traditional chinese medicine prescriptions
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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
Location sharing privacy preference: analysis and personalized recommendation
Proceedings of the 19th international conference on Intelligent User Interfaces
Time-efficient estimation of conditional mutual information for variable selection in classification
Computational Statistics & Data Analysis
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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Attribute interactions are the irreducible dependencies between attributes. Interactions underlie feature relevance and selection, the structure of joint probability and classification models: if and only if the attributes interact, they should be connected. While the issue of 2-way interactions, especially of those between an attribute and the label, has already been addressed, we introduce an operational definition of a generalized n-way interaction by highlighting two models: the reductionistic part-to-whole approximation, where the model of the whole is reconstructed from models of the parts, and the holistic reference model, where the whole is modelled directly. An interaction is deemed significant if these two models are significantly different. In this paper, we propose the Kirkwood superposition approximation for constructing part-to-whole approximations. To model data, we do not assume a particular structure of interactions, but instead construct the model by testing for the presence of interactions. The resulting map of significant interactions is a graphical model learned from the data. We confirm that the P-values computed with the assumption of the asymptotic X2 distribution closely match those obtained with the boot-strap.