A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network
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Summary
The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems.
- Type
- article
- Published
- 2017-02-01
- Cited by
- 28
- References
- 42
- Access
- Open access
- OpenAlex
- https://openalex.org/W2546487983
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:27370484
Keywords
Pruning, Computer science, Benchmark (surveying), Artificial neural network, Selection (genetic algorithm)
References
- Realization problem of multi-layer cellular neural networks
- Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions
- Real-time human action classification using a dynamic neural model
- A General Reflex Fuzzy Min-Max Neural Network
- Empirical analysis of support vector machine ensemble classifiers
- Principles of Artificial Neural Networks
- Multi-Level Fuzzy Min-Max Neural Network Classifier
- Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors
- An online pruning strategy for supervised ARTMAP-based neural networks
- Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System
- Adaptive resonance theory
- A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification
- Fuzzy min-max neural networks. I. Classification
- Ensemble-based noise detection: noise ranking and visual performance evaluation
- Evolving granular neural networks from fuzzy data streams
- A Fuzzy Min-Max Neural Network Classifier With Compensatory Neuron Architecture
- Rule Extraction: From Neural Architecture to Symbolic Representation
- Knowledge Discovery in Medical Systems Using Differential Diagnosis, LAMSTAR, and k-NN
- A neural network-based multi-agent classifier system
- Data-Core-Based Fuzzy Min–Max Neural Network for Pattern Classification
Cited by
- Design of double fuzzy clustering-driven context neural networks
- Survey of Fuzzy Min–Max Neural Network for Pattern Classification Variants and Applications
- Fuzzy Min Max Neural Network for pattern classification: An overview of complexity problem
- Hyperbox-based machine learning algorithms: a comprehensive survey
- A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
- Diagnosis of The Parkinson Disease Using Enhanced Fuzzy Min-Max Neural Network and OneR Attribute Evaluation Method
- An Improved Fuzzy Min–Max Neural Network for Data Classification
- Adaptive rough radial basis function neural network with prototype outlier removal
- A Refined Fuzzy Min–Max Neural Network With New Learning Procedures for Pattern Classification
- A Smoothing Algorithm with Constant Learning Rate for Training Two Kinds of Fuzzy Neural Networks and Its Convergence
- ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction
- Structured Pruning of Recurrent Neural Networks through Neuron Selection
- Data mining method based on rough set and fuzzy neural network
- Evolved Fuzzy Min-Max Neural Network for Unknown Labeled Data and its Application on Defect Recognition in Depth
- Class label altering fuzzy min-max network and its application to histopathology image database
- Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns
- Density-Sorting-Based Convolutional Fuzzy Min-Max Neural Network for Image Classification
- A compact fuzzy min max network with novel trimming strategy for pattern classification
- Optimization of the structural complexity of artificial neural network for hardware-driven neuromorphic computing application
- Deep Fuzzy Min–Max Neural Network: Analysis and Design
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