Mining axiomatic fuzzy set association rules for classification problems
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Publication:439483
DOI10.1016/j.ejor.2011.04.022zbMath1244.68074OpenAlexW2132965194MaRDI QIDQ439483
Publication date: 16 August 2012
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2011.04.022
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Reasoning under uncertainty in the context of artificial intelligence (68T37)
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Cites Work
- Bagging predictors
- Clustering by Passing Messages Between Data Points
- Examples, counterexamples, and measuring fuzzy associations
- Elicitation of fuzzy association rules from positive and negative examples
- Using simulated annealing to optimize the feature selection problem in marketing applications
- Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment
- A discrete particle swarm optimization method for feature selection in binary classification problems
- Axiomatic fuzzy set theory and its applications
- The fuzzy theory based on AFS algebras and AFS structure
- Fuzzy classification systems.
- Fuzzy rule based classification with FeatureSelector and modified threshold accepting
- Very simple classification rules perform well on most commonly used datasets
- On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid
- Fuzzy feature selection based on min-max learning rule and extension matrix
- Fuzzy sets and their extensions: representation, aggregation and models. Intelligent systems from decision making to data mining, web intelligence and computer vision
- Improvements to Platt's SMO Algorithm for SVM Classifier Design
- Pattern classification with principal component analysis and fuzzy rule bases
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