Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The Concentration of Fractional Distances
IEEE Transactions on Knowledge and Data Engineering
Improving Angle Based Mappings
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data
Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data
MDSpolar: a new approach for dimension reduction to visualize high dimensional data
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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Computer-based decision support can assist a medical doctor to find the right diagnosis. The knowledge and experience of the medical doctor is enhanced by a much larger data set of patients than the doctor will ever see in her or his life. The decision support system can derive possible diagnoses for a new patient based on a suitable classifier built on the patients in the patient database. However, since such a system cannot replace a medical doctor and should only support her or him, it should also provide information about the certainty of its recommendation. In this paper, we propose to visualise how close or similar the new patient is to others in the database by a modified multidimensional scaling technique that focuses on the correct positioning of the new patient in the visualisation. In this way, the medical doctor can easily see whether the diagnosis recommended by the system is reliable when all patients close to the new patient have the same diagnosis or whether it is quite uncertain when the new patient is surrounded by patients with different diagnoses.