Visualization of high-dimensional model characteristics
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
An introduction to NURBS: with historical perspective
An introduction to NURBS: with historical perspective
Visualizing and investigating multidimensional functions
VISSYM '02 Proceedings of the symposium on Data Visualisation 2002
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual sensitivity analysis for artificial neural networks
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Using sensitivity analysis and visualization techniques to open black box data mining models
Information Sciences: an International Journal
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Models of real world systems are being increasingly generated from data that describes the behaviour of systems. Data mining techniques, such as Artificial Neural Networks (ANN), generate models almost independently and deliver accurate models in a very short time. These models (sometimes called black box models) have complex internal structures that are difficult to interpret and we have very limited information about the credibility of their output. A model can be trusted just for certain configurations of input variables, but it is hard to determine which output is based on training data and which is random. In this paper, we present visualization techniques for exploration of models. Primary goal is to consider the behavior of the model in the neighborhood of the data vectors. The next goal is to estimate and locate the ranges in input space where the models are credible. We have developed visualization techniques both for regression and classification problems. Finally, we present an algorithm that is able to automatically locate the most interesting visualizations in the vast multidimensional space of input variables.