A classification scheme for applications with ambiguous data

We propose a scheme for pattern classifications in applications which include ambiguous data, that is, where pattern occupy overlapping areas in the feature space. Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first...

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Bibliographic Details
Main Authors: Trappenberg, T.P., Back, A.D.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Bayesian methods
Benchmark testing
Data mining
Linear discriminant analysis
Machine learning algorithms
Neuroscience
Pattern recognition
Psychology
Training data
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Description
Summary:We propose a scheme for pattern classifications in applications which include ambiguous data, that is, where pattern occupy overlapping areas in the feature space. Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first detect those ambiguous areas with the help of training data and then to re-classify those data in these areas as ambiguous before making class predictions on test sets. This scheme is demonstrated with a simple example and benchmarked on two real world applications.
ISSN:1098-7576
1558-3902
DOI:10.1109/IJCNN.2000.859412