Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets

DOI10.1016/j.ijar.2008.11.004zbMath1191.68497OpenAlexW2105340608WikidataQ104701660 ScholiaQ104701660MaRDI QIDQ962879

María José del Jesus, Francisco Herrera, Alberto Fernández

Publication date: 7 April 2010

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2008.11.004

zbMATH Keywords

classificationgenetic fuzzy systemsfuzzy rule based classification systemsgenetic rule selectionhierarchical fuzzy partitionsimbalanced data-sets

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (8)

Cost-Sensitive Learning of Fuzzy Rules for Imbalanced Classification Problems Using FURIA ⋮ Adaptive kernel scaling support vector machine with application to a prostate cancer image study ⋮ Automated imbalanced classification via layered learning ⋮ Identification of fuzzy inference systems by means of a multiobjective opposition-based space search algorithm ⋮ FEATURE SELECTION AND GRANULARITY LEARNING IN GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS FOR HIGHLY IMBALANCED DATA-SETS ⋮ Fuzzy qualitative trigonometry ⋮ Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming ⋮ Generating a hierarchical fuzzy rule-based model

Uses Software

• UCI-ml
• C4.5
• SMOTE

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem
• Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning
• Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation
• Cost-sensitive boosting for classification of imbalanced data
• A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 special session on real parameter optimization
• An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets
• Approximations of the critical region of the fbietkan statistic
• Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition

This page was built for publication: Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets