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Computer Science > Machine Learning

arXiv:1901.09632 (cs)
[Submitted on 28 Jan 2019]

Title:Neural eliminators and classifiers

Authors:Włodzisław Duch, Rafał Adamczak, Yoichi Hayashi
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Abstract:Classification may not be reliable for several reasons: noise in the data, insufficient input information, overlapping distributions and sharp definition of classes. Faced with several possibilities neural network may in such cases still be useful if instead of a classification elimination of improbable classes is done. Eliminators may be constructed using classifiers assigning new cases to a pool of several classes instead of just one winning class. Elimination may be done with the help of several classifiers using modified error functions. A real life medical application of neural network is presented illustrating the usefulness of elimination.
Comments: 11 pages, 1 fig
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
MSC classes: 62-07, 62G-05
ACM classes: I.2.6
Cite as: arXiv:1901.09632 [cs.LG]
  (or arXiv:1901.09632v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1901.09632
Journal reference: Early version presented at 7th International Conference on Neural Information Processing 2000 (ICONIP), Dae-jong, Korea, ed. by Soo-Young Lee, pp. 1029-1034

Submission history

From: Wlodzislaw Duch [view email]
[v1] Mon, 28 Jan 2019 12:57:32 UTC (25 KB)
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