Neural eliminators and classifiers
This work addresses classification reliability issues in domains like medical diagnosis, but it appears incremental as it builds on existing classifier methods with modifications.
The paper tackles the problem of unreliable classification due to noise, insufficient information, overlapping distributions, and sharp class definitions by proposing neural eliminators that eliminate improbable classes instead of direct classification, and demonstrates its usefulness in a real-life medical application.
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.