The cascade-correlation learning architecture
Advances in neural information processing systems 2
Neural network constructive algorithms: trading generalization for learning efficiency?
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
A penalty-function approach for pruning feedforward neural networks
Neural Computation
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Investigation of the CasCor family of learning algorithms
Neural Networks
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
Applied Intelligence
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
The Influence of Parameters in Evolutionary Based Rule Extraction Method from Neural Network
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Discovering the Mysteries of Neural Networks
International Journal of Hybrid Intelligent Systems
Learning and generalization in cascade network architectures
Neural Computation
Uniqueness of medical data mining
Artificial Intelligence in Medicine
A model for single and multiple knowledge based networks
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Extracting rules from trained neural networks
IEEE Transactions on Neural Networks
Extracting M-of-N rules from trained neural networks
IEEE Transactions on Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Mutation-based genetic neural network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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The application of neural networks in the data mining has become wider. Although neural networks may have complex structure, long training time, and the representation of results is not comprehensible, neural networks have high acceptance ability for noisy data, high accuracy and are preferable in data mining. On the other hand, It is an open question as to what is the best way to train and extract symbolic rules from trained neural networks in domains like classification. In this paper, we train the neural networks by constructive learning and present the analysis of the convergence rate of the error in a neural network with and without threshold which have been learnt by a constructive method to obtain the simple structure of the network. The response of ANN is acquired but its result is not in understandable form or in a black box form. It is frequently desirable to use the model backwards and identify sets of input variable which results in a desired output value. The large numbers of variables and nonlinear nature of many materials models that can help finding an optimal set of difficult input variables. We will use a genetic algorithm to solve this problem. The method is evaluated on different public-domain data sets with the aim of testing the predictive ability of the method and compared with standard classifiers, results showed comparatively high accuracy.