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Handling Classification Uncertainty


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  • Product Description

Many diagnostic applications require not only the samples to be classified, but a certainty value is required in addition to the predicted class label indicating the strength of the diagnosis. The first part of this Thesis proposes an extension to the decision tree framework to handle classification uncertainty, involving distance calculation to the relevant decision boundary; class density, correct classification probability and confidence estimation. The method is also applicable to trees that utilize oblique hyperplanes to cluster the input space and it is not restricted to the Euclidian distance metric. In the second part it is shown that these classification confidence values can be integrated to derive a consensus decision. Using the proposed combination scheme there is no need for an auxiliary combiner or weighting network, the weights are adaptively provided by the individual tree classifiers in the ensemble, new classifiers can be added dynamically without any retraining or modification to the existing system. This discussion can give a head start to anyone – researcher or developer – dealing with problems, where classification certainty is an issue.

Product Specifications
SKU :COC38763
AuthorNorbert Tóth
Number of Pages152
Publishing Year2010-03-09T00:00:00.000
Edition1 st
Book TypeComputing & information technology
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-01-08 00:00:00