Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural networks and the bias/variance dilemma
Neural Computation
Genetic recursive regression for modeling and forecasting real-world chaotic time series
Advances in genetic programming
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Data Mining: A Heuristic Approach
Data Mining: A Heuristic Approach
Supervised Training Using an Unsupervised Approach to Active Learning
Neural Processing Letters
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Generating Linear Regression Rules from Neural Networks Using Local Least Squares Approximation
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Pruned neural networks for regression
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Foundations of Genetic Programming
Foundations of Genetic Programming
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Use of a quasi-Newton method in a feedforward neural network construction algorithm
IEEE Transactions on Neural Networks
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
Two layered Genetic Programming for mixed-attribute data classification
Applied Soft Computing
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This paper presents a genetic programming (GP) approach to extract symbolic rules from data sets with continuous-valued classes, called GPMCC. The GPMCC makes use of a genetic algorithm (GA) to evolve multi-variate non-linear models [Potgieter, G., & Engelbrecht, A. (2007). Genetic algorithms for the structural optimisation of learned polynomial expressions. Applied Mathematics and Computation] at the terminal nodes of the GP. Several mechanisms have been developed to optimise the GP, including a fragment pool of candidate non-linear models, k-means clustering of the training data to facilitate the use of stratified sampling methods, and specialized mutation and crossover operators to evolve structurally optimal and accurate models. It is shown that the GPMCC is insensitive to control parameter values. Experimental results show that the accuracy of the GPMCC is comparable to that of NeuroLinear and Cubist, while producing significantly less rules with less complex antecedents.