Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Gaining insights into support vector machine pattern classifiers using projection-based tour methods
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Graphics for Statistics
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VIS '94 Proceedings of the conference on Visualization '94
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Parallel Sets: Visual Analysis of Categorical Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
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Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
VisLink: Revealing Relationships Amongst Visualizations
IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
Towards Effective Visual Data Mining with Cooperative Approaches
Visual Data Mining
Interactive Graphics for Data Analysis: Principles and Examples (Computer Science and Data Analysis)
Interactive Graphics for Data Analysis: Principles and Examples (Computer Science and Data Analysis)
IEEE Computer Graphics and Applications
Visual data mining of multimedia data for social and behavioral studies
Information Visualization
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IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
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Communications of the ACM
Visual Thinking: for Design
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In risk assessment applications well-informed decisions need to be made based on large amounts of multidimensional data. In many domains, not only the risk of a wrong decision, but also of the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework to support the decision-making process, which tightly integrates interactive visual exploration with machine learning. The proposed approach uses a series of interactive 2D visualizations of numerical and ordinal data combined with visualization of classification models. These series of visual elements are linked to the classifier's performance, which is visualized using an interactive performance curve. This interaction allows the decision-maker to steer the classification model and instantly identify the critical, cost-changing data elements in the various linked visualizations. The critical data elements are represented as images in order to trigger associations related to the knowledge of the expert. In this way the data visualization and classification results are not only linked together, but are also linked back to the classification model. Such a visual analytics framework allows the user to interactively explore the costs of his decisions for different settings of the model and, accordingly, use the most suitable classification model. More informed and reliable decisions result. A case study in the forensic psychiatry domain reveals the usefulness of the suggested approach.