Neural eliminators and classifiers

Włodzisław Duch; Rafał Adamczak; Yoichi Hayashi

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arXiv · 1901.09632

Neural eliminators and classifiers

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.

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Włodzisław Duch, Rafał Adamczak, Yoichi Hayashi. 2019-01-28. Neural eliminators and classifiers. https://arxiv.org/abs/1901.09632

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