Inferring decision trees using the minimum description length principle
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
A search technique for rule extraction from trained neural networks
Non-Linear Analysis
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
Knowledge Acquisition from Databases
Knowledge Acquisition from Databases
Induction By Attribute Elimination
IEEE Transactions on Knowledge and Data Engineering
Extracting decision trees from trained neural networks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Computational Optimization and Applications
Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
Dynamic data mining technique for rules extraction in a process of battery charging
Applied Soft Computing
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
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
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Information extraction for search engines using fast heuristic techniques
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
Rule extraction from support vector machines: A review
Neurocomputing
Determining optimal sensor locations in freeway using genetic algorithm-based optimization
Engineering Applications of Artificial Intelligence
Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data
Artificial Intelligence in Medicine
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
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This paper introduces a novel classification model for cotton yarn quality. The proposed model is composed of two major techniques namely: Artificial Neural Network (ANN) and genetic algorithm (GA). First, training the ANN on encoding database to extract the weights between input and hidden layer, and hidden and output layer. Consequently, the output function for each output node of ANN can be constructed as a function of input attributes values and the specific obtained weights. This function is nonlinear exponential function depending only on the values of input attributes. Second, the genetic algorithm is used to find the optimal values of the input chromosomes (attributes) which maximize the nonlinear exponential function of the output node of ANN. Finally, the results of the optimum chromosomes are decoded and used to get a rule belonging to a specific class.