Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Fuzzy MLP based expert system for medical diagnosis
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
A search technique for rule extraction from trained neural networks
Non-Linear Analysis
Journal of Global Optimization
Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
Computational Intelligence: for Engineering and Manufacturing
Computational Intelligence: for Engineering and Manufacturing
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Hi-index | 12.05 |
Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous system. They have the ability of predicting, learning from experiences and generalizing from previous examples. An important drawback of ANNs is their very limited explanation capability, mainly due to the fact that knowledge embedded within ANNs is distributed over the activations and the connection weights. Therefore, one of the main challenges in the recent decades is to extract classification rules from ANNs. This paper presents a novel approach to extract fuzzy classification rules (FCR) from ANNs because of the fact that fuzzy rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. A soft computing based algorithm is developed to generate fuzzy rules based on a data mining tool (DIFACONN-miner), which was recently developed by the authors. Fuzzy DIFACONN-miner algorithm can extract fuzzy classification rules from datasets containing both categorical and continuous attributes. Experimental research on the benchmark datasets and comparisons with other fuzzy rule based classification (FRBC) algorithms has shown that the proposed algorithm yields high classification accuracies and comprehensible rule sets.