Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems
From MaRDI portal
Publication:1602487
DOI10.1016/S0020-0255(01)00147-5zbMath0996.68158WikidataQ62608504 ScholiaQ62608504MaRDI QIDQ1602487
Jorge Casillas, Francisco Herrera, María José del Jesus, Oscar Cordón
Publication date: 23 June 2002
Published in: Information Sciences (Search for Journal in Brave)
Related Items
A proposed method for learning rule weights in fuzzy rule-based classification systems ⋮ A multilayered neuro-fuzzy classifier with self-organizing properties ⋮ Extracting complex linguistic data summaries from personnel database via simple linguistic aggregations ⋮ Sensitivity analysis of fuzzy rule-based classification systems by means of the Lipschitz condition ⋮ A genetically modified fuzzy linear discriminant analysis for face recognition ⋮ On-line incremental feature weighting in evolving fuzzy classifiers ⋮ Ten years of genetic fuzzy systems: Current framework and new trends. ⋮ FEATURE SELECTION AND GRANULARITY LEARNING IN GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS FOR HIGHLY IMBALANCED DATA-SETS ⋮ IFS-CoCo: instance and feature selection based on cooperative coevolution with nearest neighbor rule ⋮ A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System ⋮ An efficient multi-objective evolutionary fuzzy system for regression problems
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples.
- Wrappers for feature subset selection
- A genetic algorithm for generating fuzzy classification rules
- Formulation of a multivalued recognition system
- A note on genetic algorithms for large-scale feature selection
- Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
- Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems