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
Hybrid Classifiers for Financial Multicriteria Decision Making: TheCase of Bankruptcy Prediction
Computational Economics
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
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Software Reliability Prediction Using Wavelet Neural Networks
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
Software development cost estimation using wavelet neural networks
Journal of Systems and Software
Expert Systems with Applications: An International Journal
International Journal of Bio-Inspired Computation
IEEE Transactions on Fuzzy Systems
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This paper proposes a method to extract rules from differential evolution trained wavelet neural network (DEWNN) [1]. for solving classification and regression problems. The rule generation methods viz., Decision Tree (DT), Ripper and Classification and Regression Tree (CART) and Dynamic Evolving Neuro Fuzzy Inference System (DENFIS) are employed to extract rules from DEWNN for classification and regression problems respectively. The feature selection algorithm adapted by Chauhan et al., [1] is used in the present study. The effectiveness of the proposed hybrid is evaluated on Iris, Wine and four bankruptcy prediction datasets namely Spanish banks, Turkish banks, US banks, UK banks and Auto MPG dataset, Body fat dataset, Boston Housing dataset, Forest Fires dataset, Pollution dataset, by using 10-fold cross validation. From the results, it is concluded that the proposed hybrid method performed well in terms of sensitivity in classification problems.