Communications of the ACM
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
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: Concepts and Techniques
Data Mining: Concepts and Techniques
Anomaly detection in TCP/IP networks using immune systems paradigm
Computer Communications
Expert Systems with Applications: An International Journal
A new approach for epileptic seizure detection using adaptive neural network
Expert Systems with Applications: An International Journal
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Artificial immune algorithm for IIR filter design
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
A novel genetic programming based approach for classification problems
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
Although Artificial Neural Network (ANN) may achieve high accuracy of classification, the knowledge acquired by them is incomprehensible for humans. 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. Selection of the activation function is important in the performance of ANN. Networks with adaptive activation function seem to provide better fitting properties than classical architectures with fixed activation function neurons [1]. In this study, first neural network has been trained with adaptive activation function. Then for the purpose of extracting rules from adaptive ANN which has been trained for classification, OptaiNET that is an Artificial Immune Algorithm (AIS) has been used and a set of rules has been formed for liver disorder.