Communications of the ACM
C4.5: programs for machine learning
C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Rule extraction from trained neural networks using genetic algorithms
Proceedings of the second world congress on Nonlinear analysts: part 3
A search technique for rule extraction from trained neural networks
Non-Linear Analysis
Applying GMDH algorithm to extract rules from examples
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
Literal and ProRulext: Algorithms for Rule Extraction of ANNs
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Soft computing system for bank performance prediction
Applied Soft Computing
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Dynamic data mining technique for rules extraction in a process of battery charging
Applied Soft Computing
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
Letters: Energy demand prediction using GMDH networks
Neurocomputing
Extracting rules for classification problems: AIS based approach
Expert Systems with Applications: An International Journal
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Software Reliability Prediction Using Group Method of Data Handling
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Kernel group method of data handling: application to regression problems
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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This paper proposes a hybrid method to extract rules from the trained Group Method of Data Handling (GMDH) neural network using Decision Tree (DT). The outputs predicted by the GMDH for the training set along with the input variables are fed to the DT for extracting the rules. The effectiveness of the proposed hybrid is evaluated on four benchmark datasets namely Iris, Wine, US Congressional, New Thyroid and one small scale data mining dataset churn prediction using 10-fold cross-validation. One important conclusion from the study is that we obtained statistically significant accuracies at 1% level in the case of churn prediction and IRIS datasets. Further, in the present study, we noticed that the rule base size of proposed hybrid is less in churn prediction and IRIS datasets when compared to that of the DT and equal in the case of remaining datasets.