Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Swarm Optimisation as a New Tool for Data Mining
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Improving particle swarm optimization with differentially perturbed velocity
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Computational Optimization and Applications
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Neural network explanation using inversion
Neural Networks
Rule Mining Algorithm with a New Ant Colony Optimization Algorithm
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
A hybrid PSO/ACO algorithm for discovering classification rules in data mining
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications
Learning and Intelligent Optimization
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
A PSO-Based Classification Rule Mining Algorithm
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Expert Systems with Applications: An International Journal
Classification rule mining with an improved ant colony algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial Intelligence
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Purpose: Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence. Method: Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function. Results: The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers.