Construction, visualization and application of neutral zone classifiers
| Autor: | Daniel R. Jeske, Zhiwei Zhang, Steven S. Smith |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Epidemiology business.industry Computer science Neutral zone Pattern recognition 01 natural sciences Visualization 010104 statistics & probability 03 medical and health sciences Statistical classification 0302 clinical medicine Health Information Management 030212 general & internal medicine Artificial intelligence 0101 mathematics business Classifier (UML) Statistical classifier |
| Zdroj: | Statistical Methods in Medical Research. 29:1420-1433 |
| ISSN: | 1477-0334 0962-2802 |
| DOI: | 10.1177/0962280219863823 |
| Popis: | When the potential for making accurate classifications with a statistical classifier is limited, a neutral zone classifier can be constructed by adding a no-decision option as a classification outcome. We show how a neutral zone classifier can be constructed from a receiving operating characteristic (ROC) curve. We extend the ROC curve graphic to highlight important performance characteristics of a neutral zone classifier. Additional utility of neutral zone classifiers is illustrated by showing how they can be incorporated into the first stage of a two-stage classification process. At the first stage, a classification is attempted from easily collected or inexpensive features. If the classification falls into the neutral zone, additional relatively more expensive features can be obtained and used to make a definitive classification at the second stage. The methods discussed in the paper are illustrated with an application pertaining to prostate cancer. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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