Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Functional Trees for Classification
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Lazy Approach to Pruning Classification Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Addressing the Problems of Bayesian Network Classification of Video Using High-Dimensional Features
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
Fuzzy classifier design using genetic algorithms
Pattern Recognition
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification
Expert Systems with Applications: An International Journal
A currency crisis and its perception with fuzzy C-means
Information Sciences: an International Journal
Numerical solution of a system of fuzzy polynomials by fuzzy neural network
Information Sciences: an International Journal
Fuzzy rough set theory for the interval-valued fuzzy information systems
Information Sciences: an International Journal
Generating fuzzy rules from training instances for fuzzy classification systems
Expert Systems with Applications: An International Journal
On fuzzy approximation operators in attribute reduction with fuzzy rough sets
Information Sciences: an International Journal
Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning
Information Sciences: an International Journal
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
Computers and Industrial Engineering
Design of hierarchical fuzzy model for classification problem using GAs
Computers and Industrial Engineering
Dynamic projection network for supervised pattern classification
International Journal of Approximate Reasoning
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
IEEE Transactions on Neural Networks
A Fuzzy Min-Max Neural Network Classifier With Compensatory Neuron Architecture
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature.