Confidence sets for statistical classification (II): Exact confidence sets
Confidence sets for statistical classification (II): Exact confidence sets
Classification has applications in a wide range of fields including medicine, engineering, computer science and social sciences among others. Liu et al. (2019) proposed a confidence-set-based classifier that classifies a future object into a single class only when there is enough evidence to warrant this, and into several classes otherwise. By allowing classification of an object into possibly more than one class, this classifier guarantees a pre-specified proportion of correct classification among all future objects. However, the classifier uses a conservative critical constant. In this paper, we show how to determine the exact critical constant in applications where prior knowledge about the proportions of the future objects from each class is available. As the exact critical constant is smaller than the conservative critical constant given by Liu et al. (2019), the classifier using the exact critical constant is better than the classifier by Liu et al. (2019) as expected. An example is provided to illustrate the method
439-446
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Bretz, Frank
51270819-e491-4a72-a410-679d86231e64
Hayter, Anthony
841aec34-bd38-42bb-974c-e3de4752ac38
7 November 2019
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Bretz, Frank
51270819-e491-4a72-a410-679d86231e64
Hayter, Anthony
841aec34-bd38-42bb-974c-e3de4752ac38
Liu, Wei, Bretz, Frank and Hayter, Anthony
(2019)
Confidence sets for statistical classification (II): Exact confidence sets.
Stats, 2 (4), .
(doi:10.3390/stats2040030).
Abstract
Classification has applications in a wide range of fields including medicine, engineering, computer science and social sciences among others. Liu et al. (2019) proposed a confidence-set-based classifier that classifies a future object into a single class only when there is enough evidence to warrant this, and into several classes otherwise. By allowing classification of an object into possibly more than one class, this classifier guarantees a pre-specified proportion of correct classification among all future objects. However, the classifier uses a conservative critical constant. In this paper, we show how to determine the exact critical constant in applications where prior knowledge about the proportions of the future objects from each class is available. As the exact critical constant is smaller than the conservative critical constant given by Liu et al. (2019), the classifier using the exact critical constant is better than the classifier by Liu et al. (2019) as expected. An example is provided to illustrate the method
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Accepted/In Press date: 1 November 2019
Published date: 7 November 2019
Identifiers
Local EPrints ID: 437170
URI: http://eprints.soton.ac.uk/id/eprint/437170
ISSN: 2571-905X
PURE UUID: 92e25725-6ee1-467d-b89d-a7638cb4de46
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Date deposited: 20 Jan 2020 17:34
Last modified: 17 Mar 2024 02:37
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Author:
Frank Bretz
Author:
Anthony Hayter
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