Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Interpretation of Trained Neural Networks by Rule Extraction
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
SVM and Graphical Algorithms: A Cooperative Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Nomograms for visualization of naive Bayesian classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Nomograms for visualizing support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Explaining Classifications For Individual Instances
IEEE Transactions on Knowledge and Data Engineering
Visual explanation of evidence in additive classifiers
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
An Efficient Explanation of Individual Classifications using Game Theory
The Journal of Machine Learning Research
Explanation and reliability of prediction models: the case of breast cancer recurrence
Knowledge and Information Systems
Efficiently explaining decisions of probabilistic RBF classification networks
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
An overview on subgroup discovery: foundations and applications
Knowledge and Information Systems
Shared kernel models for class conditional density estimation
IEEE Transactions on Neural Networks
An incremental training method for the probabilistic RBF network
IEEE Transactions on Neural Networks
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A probabilistic radial basis function PRBF network is an effective non-linear classifier. However, similar to most other neural network models it is non-transparent, which makes its predictions difficult to interpret. In this paper we show how a one-variable-at-a-time and an all-subsets explanation method can be modified for an equivalent and more efficient use with PRBF network classifiers. We use several artificial and real-life data sets to demonstrate the usefulness of the visualizations and explanations of the PRBF network classifier.