Algorithms for clustering data
Algorithms for clustering data
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
An introduction to variable and feature selection
The Journal of Machine Learning Research
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Financial market trading system with a hierarchical coevolutionary fuzzy predictive model
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Artificial Life
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
Decision support systems are powerful technologies for complex decision making and problem solving. However, constructing an accurate and interpretable decision support system (DSS) for any domain is a challenge. In this paper, a novel hierarchical co-evolutionary fuzzy system called HiCEFS is presented that can autonomously derive a fuzzy rule-based DSS from exemplar data. Most of the important components in HiCEFS, including irregular shaped membership functions (ISMFs) and fuzzy rules, are generated using a hierarchical co-evolutionary genetic algorithm that simultaneously co-evolves these components in separate genetic populations. Owing to its generic learning capability, the HiCEFS approach can be easily applied to produce DSSs for classification and regression tasks in various domains. As a case study, HiCEFS is employed to construct a DSS for detecting gamma ray signals. Experimental results show that the system is able to successfully discern the gamma rays from background hadrons, and performs superior to other established techniques.