On the Behaviour of Information Measures for Test Selection

D. Sent; L.C. van der Gaag

doi:10.1007/978-3-540-73599-1_42

On the Behaviour of Information Measures for Test Selection

D. Sent, L.C. van der Gaag

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

tests that are expected to yield the largest decrease in the uncertainty about a patient’s diagnosis. For capturing diagnostic uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that for a large range of values, the first derivative of the Gini index can be regarded as an approximation of the first derivative of the Shannon entropy. We also argue that the differences between the derivative functions outside this range can explain different test sequences in practice. We further argue that the misclassification error is less suited for test-selection purposes as it is likely to show a tendency to select tests arbitrarily. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations.

Original language	Undefined
Title of host publication	Proceedings of Artifical Intelligence in Medicine Europe (AIME)
Editors	R Bellazzi, A Abu-Hanna, J Hunter
Place of Publication	Berlin
Publisher	Springer
Pages	316-326
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-540-73599-1_42
Publication status	Published - 2007
Event	11th Conference on Artificial Intelligence in Medicine, AIME 2007 - Amsterdam, Netherlands Duration: 7 Jul 2007 → 11 Jul 2007 Conference number: 11

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer Verlag
Number	LNCS4549
Volume	4594
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Conference on Artificial Intelligence in Medicine, AIME 2007
Abbreviated title	AIME
Country/Territory	Netherlands
City	Amsterdam
Period	7/07/07 → 11/07/07

Keywords

EWI-10747
METIS-241770
IR-61840
misclassification error
Shannon entropy
Test selection
Gini index

Access to Document

10.1007/978-3-540-73599-1_42

http://eprints.eemcs.utwente.nl/secure2/10747/01/45940316.pdf

Cite this

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title = "On the Behaviour of Information Measures for Test Selection",

abstract = "tests that are expected to yield the largest decrease in the uncertainty about a patient{\textquoteright}s diagnosis. For capturing diagnostic uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that for a large range of values, the first derivative of the Gini index can be regarded as an approximation of the first derivative of the Shannon entropy. We also argue that the differences between the derivative functions outside this range can explain different test sequences in practice. We further argue that the misclassification error is less suited for test-selection purposes as it is likely to show a tendency to select tests arbitrarily. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations.",

keywords = "EWI-10747, METIS-241770, IR-61840, misclassification error, Shannon entropy, Test selection, Gini index",

author = "D. Sent and {van der Gaag}, L.C.",

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series = "Lecture Notes in Artificial Intelligence",

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editor = "R Bellazzi and A Abu-Hanna and J Hunter",

booktitle = "Proceedings of Artifical Intelligence in Medicine Europe (AIME)",

address = "Germany",

note = "11th Conference on Artificial Intelligence in Medicine, AIME 2007 ; Conference date: 07-07-2007 Through 11-07-2007",

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Sent, D & van der Gaag, LC 2007, On the Behaviour of Information Measures for Test Selection. in R Bellazzi, A Abu-Hanna & J Hunter (eds), Proceedings of Artifical Intelligence in Medicine Europe (AIME). Lecture Notes in Artificial Intelligence, no. LNCS4549, vol. 4594, Springer, Berlin, pp. 316-326, 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Amsterdam, Netherlands, 7/07/07. https://doi.org/10.1007/978-3-540-73599-1_42

On the Behaviour of Information Measures for Test Selection. / Sent, D.; van der Gaag, L.C.
Proceedings of Artifical Intelligence in Medicine Europe (AIME). ed. / R Bellazzi; A Abu-Hanna; J Hunter. Berlin: Springer, 2007. p. 316-326 (Lecture Notes in Artificial Intelligence; Vol. 4594, No. LNCS4549).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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