C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
Use of Neural Networks for Prediction of Graft Failure following Liver Transplantation
CBMS '95 Proceedings of the Eighth Annual IEEE Symposium on Computer-Based Medical Systems
Using artificial neural network ensembles to extract data content from noisy data
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
| Hi-index | 0.00 |
The learning strategy employed in neural networks offers a good performance even in the situations where a model is presented with incomplete and noisy data. However, neural networks are known as ‘black boxes' as how the outputs are produced is not clear. In this study, a hybrid learning strategy, namely RDC-ANNE (Rules Driven by Consistency in Artificial Neural Networks Ensemble) is proposed. This paper looks at the use of RDC-ANNE in the graft outcome prediction domain as a prototypical medical application. At first, for a better generalization, a committee of binary neural networks is trained. Then, a partial C4.5 decision tree is built from a specifically selected dataset, generated based on the graft data used to test the trained neural networks ensemble. Finally the most appropriate leaf in every path is converted into an understandable rule. In this approach, for the rule generation process, we enforced the model to mainly consider the patterns that their class labels were consistently causing agreement across the neural network classifiers. Experimental results show that the RDC-ANNE method is able to extract partial rules from an ensemble model and reveal the important embedded information of a trained neural network ensemble.