Communications of the ACM
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
FERNN: An Algorithm for Fast Extraction of Rules fromNeural Networks
Applied Intelligence
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
Justification of a Neuron-Adaptive Activation Function
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Data Mining " An Adaptive Neural Network Model for Financial Analysis
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
IEEE Transactions on Knowledge and Data Engineering
Neural network explanation using inversion
Neural Networks
Anomaly detection in TCP/IP networks using immune systems paradigm
Computer Communications
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
ACS'08 Proceedings of the 8th conference on Applied computer scince
A hybrid approach to design efficient learning classifiers
Computers & Mathematics with Applications
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
An overview of the use of neural networks for data mining tasks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
Prediction of the Amount of Wood Using Neural Networks
Journal of Mathematical Modelling and Algorithms
Information Sciences: an International Journal
Hi-index | 12.05 |
Although artificial neural network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. Activation function is critical as the behavior and performance of an ANN model largely depends on it. So far there have been limited studies with emphasis on setting a few free parameters in the neuron activation function. ANN's with such activation function seem to provide better fitting properties than classical architectures with fixed activation function neurons [Xu, S., & Zhang, M. (2005). Data mining - An adaptive neural network model for financial analysis. In Proceedings of the third international conference on information technology and applications]. In this study a new method that uses artificial immune systems (AIS) algorithm has been presented to extract rules from trained adaptive neural network. Two real time problems data were investigated for determining applicability of the proposed method. The data were obtained from University of California at Irvine (UCI) machine learning repository. The datasets were obtained from Breast Cancer disease and ECG data. The proposed method achieved accuracy values 94.59% and 92.31% for ECG and Breast Cancer dataset, respectively. It has been observed that these results are one of the best results comparing with results obtained from related previous studies and reported in UCI web sites.