C4.5: programs for machine learning
C4.5: programs for machine learning
Principles of data mining
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Journal of Global Optimization
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
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
Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems
Fuzzy Optimization and Decision Making
The Influence of Parameters in Evolutionary Based Rule Extraction Method from Neural Network
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Computational Optimization and Applications
A new version of the ant-miner algorithm discovering unordered rule sets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A Parallel Differential Evolution Algorithm A Parallel Differential Evolution Algorithm
PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Computational Intelligence: for Engineering and Manufacturing
Computational Intelligence: for Engineering and Manufacturing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Neural network explanation using inversion
Neural Networks
A new approach to classification based on association rule mining
Decision Support Systems
A greedy classification algorithm based on association rule
Applied Soft Computing
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Classifying defect factors in fabric production via DIFACONN-miner: A case study
Expert Systems with Applications: An International Journal
Training neural networks with harmony search algorithms for classification problems
Engineering Applications of Artificial Intelligence
Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks
Expert Systems with Applications: An International Journal
Differential Evolution for automatic rule extraction from medical databases
Applied Soft Computing
International Journal of Data Mining and Bioinformatics
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Artificial neural network (ANN) is one of the most widely used techniques in classification data mining. Although ANNs can achieve very high classification accuracies, their explanation capability is very limited. Therefore one of the main challenges in using ANNs in data mining applications is to extract explicit knowledge from them. Based on this motivation, a novel approach is proposed in this paper for generating classification rules from feed forward type ANNs. Although there are several approaches in the literature for classification rule extraction from ANNs, the present approach is fundamentally different from them. In the previous studies, ANN training and rule extraction is generally performed independently in a sequential (hierarchical) manner. However, in the present study, training and rule extraction phases are integrated within a multiple objective evaluation framework for generating accurate classification rules directly. The proposed approach makes use of differential evolution algorithm for training and touring ant colony optimization algorithm for rule extracting. The proposed algorithm is named as DIFACONN-miner. Experimental study on the benchmark data sets and comparisons with some other classical and state-of-the art rule extraction algorithms has shown that the proposed approach has a big potential to discover more accurate and concise classification rules.