A Classification Scheme for Applications with Ambiguous Data

  title={A Classification Scheme for Applications with Ambiguous Data},
  author={Thomas P. Trappenberg and Andrew D. Back},
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… CONTINUE READING

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