Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Rule-Based Learning Systems for Support Vector Machines
Neural Processing Letters
Letters: Support vector perceptrons
Neurocomputing
Discovering the Mysteries of Neural Networks
International Journal of Hybrid Intelligent Systems
Pruning extensions to stacking
Intelligent Data Analysis
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
Letters: Support vector machine interpretation
Neurocomputing
Intelligent techniques for cigarette formula design
Mathematics and Computers in Simulation
Use of vegetation index and meteorological parameters for the prediction of crop yield in India
International Journal of Remote Sensing
Evolving model trees for mining data sets with continuous-valued classes
Expert Systems with Applications: An International Journal
Knowledge discovery in corporate events by neural network rule extraction
Applied Intelligence
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Extracting rules for classification problems: AIS based approach
Expert Systems with Applications: An International Journal
A fusion of stacking with dynamic integration
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bidirectional Clustering of MLP Weights for Finding Nominally Conditioned Polynomials
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Knowledge-internalization process for neural-networks practitioners
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Selective enhancement learning in competitive learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
On a hybrid weightless neural system
International Journal of Bio-Inspired Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
ITER: an algorithm for predictive regression rule extraction
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Prediction of the Amount of Wood Using Neural Networks
Journal of Mathematical Modelling and Algorithms
An Abductive-Reasoning Guide for Finance Practitioners
Computational Economics
Hi-index | 0.01 |
Neural networks (NNs) have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful as function approximators because they do not require prior knowledge of the input data distribution and they have been shown to be universal approximators. In many applications, it is desirable to extract knowledge that can explain how Me problems are solved by the networks. Most existing approaches have focused on extracting symbolic rules for classification. Few methods have been devised to extract rules from trained NNs for regression. This article presents an approach for extracting rules from trained NNs for regression. Each rule in the extracted rule set corresponds to a subregion of the input space and a linear function involving the relevant input attributes of the data approximates the network output for all data samples in this subregion. Extensive experimental results on 32 benchmark data sets demonstrate the effectiveness of the proposed approach in generating accurate regression rules