Iterative ranking-and-selection for large-scale optimization
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Ordinal Comparison via the Nested Partitions Method
Discrete Event Dynamic Systems
Families of splitting criteria for classification trees
Statistics and Computing
Machine Learning
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the 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
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Nested Partitions Method for Global Optimization
Operations Research
Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
An incremental nested partition method for data clustering
Pattern Recognition
A new hybrid algorithm for feature selection and its application to customer recognition
COCOA'07 Proceedings of the 1st international conference on Combinatorial optimization and applications
Hybrid nested partitions algorithm for scheduling in job shop problem
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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This paper develops a new optimization-based feature-selection framework for knowledge discovery in databases. Algorithms following this new framework have attractive theoretical properties such as proven convergence to an optimal set of relevant features and the ability for deriving rigorous statements regarding the quality of the set that is found. Within this framework both wrapper and filter algorithms are derived, and numerical experiments show the new methodology to perform well with respect to accuracy and simplicity of the set of features found to be relevant.