C4.5: programs for machine learning
C4.5: programs for machine learning
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Extraction of Logical Rules from Neural Networks
Neural Processing Letters
Rule extraction by successive regularization
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Extracting Interpretable Fuzzy Rules from RBF Networks
Neural Processing Letters
Advanced Fuzzy Systems Design and Applications
Advanced Fuzzy Systems Design and Applications
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
A dynamical system perspective of structural learning with forgetting
IEEE Transactions on Neural Networks
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Training neural networks using multiobjective particle swarm optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
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It has been a controversial issue in the research of cognitive science and artificial intelligence whether signal-type representations (typically connectionist networks) or symbol-type representations (e.g., semantic networks, production systems) should be used. Meanwhile, it has also been recognized that both types of information representations might exist in the human brain. In addition, symbol-type representations are often very helpful in gaining insights into unknown systems. For these reasons, comprehensible symbolic rules need to be extracted from trained neural networks. In this paper, an evolutionary multi-objective algorithm is employed to generate multiple models that facilitate the generation of signal-type and symbol-type representations simultaneously. It is argued that one main difference between signal-type and symbol-type representations lies in the fact that the signal-type representations are models of a higher complexity (fine representation), whereas symbol-type representations are models of a lower complexity (coarse representation). Thus, by generating models with a spectrum of model complexity, we are able to obtain a population of models of both signal-type and symbol-type quality, although certain post-processing is needed to get a fully symbol-type representation. An illustrative example is given on generating neural networks for the breast cancer diagnosis benchmark problem.