Enhancing Automated Test Selection in Probabilistic Networks

D. Sent; L.C. van der Gaag

doi:10.1007/978-3-540-73599-1_44

Enhancing Automated Test Selection in Probabilistic Networks

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

Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, test variables are selected sequentially, on a one-by-one basis, based upon expected information gain. While myopic test selection is not realistic for many medical applications, non-myopic test selection, in which information gain would be computed for all combinations of variables, would be too demanding. We present three new test-selection algorithms for probabilistic networks, which all employ knowledge-based clusterings of variables; these are a myopic algorithm, a non-myopic algorithm and a semi-myopic algorithm. In a preliminary evaluation study, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.

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	331-335
Number of pages	5
DOIs	https://doi.org/10.1007/978-3-540-73599-1_44
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

probabilistic networks
semi-myopia
diagnostic test selection
METIS-241771
IR-61841
EWI-10748

Access to Document

10.1007/978-3-540-73599-1_44

Cite this

@inproceedings{195116fb307040c8ab2a01395b175bb4,

title = "Enhancing Automated Test Selection in Probabilistic Networks",

abstract = "Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, test variables are selected sequentially, on a one-by-one basis, based upon expected information gain. While myopic test selection is not realistic for many medical applications, non-myopic test selection, in which information gain would be computed for all combinations of variables, would be too demanding. We present three new test-selection algorithms for probabilistic networks, which all employ knowledge-based clusterings of variables; these are a myopic algorithm, a non-myopic algorithm and a semi-myopic algorithm. In a preliminary evaluation study, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.",

keywords = "probabilistic networks, semi-myopia, diagnostic test selection, METIS-241771, IR-61841, EWI-10748",

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

note = "http://eprints.ewi.utwente.nl/10748 ; 11th Conference on Artificial Intelligence in Medicine, AIME 2007 ; Conference date: 07-07-2007 Through 11-07-2007",

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language = "Undefined",

series = "Lecture Notes in Artificial Intelligence",

publisher = "Springer",

number = "LNCS4549",

pages = "331--335",

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

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

address = "Germany",

}

Sent, D & van der Gaag, LC 2007, Enhancing Automated Test Selection in Probabilistic Networks. 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. 331-335, 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Amsterdam, Netherlands, 7/07/07. https://doi.org/10.1007/978-3-540-73599-1_44

Enhancing Automated Test Selection in Probabilistic Networks. / 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. 331-335 (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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AU - van der Gaag, L.C.

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N2 - Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, test variables are selected sequentially, on a one-by-one basis, based upon expected information gain. While myopic test selection is not realistic for many medical applications, non-myopic test selection, in which information gain would be computed for all combinations of variables, would be too demanding. We present three new test-selection algorithms for probabilistic networks, which all employ knowledge-based clusterings of variables; these are a myopic algorithm, a non-myopic algorithm and a semi-myopic algorithm. In a preliminary evaluation study, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.

AB - Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, test variables are selected sequentially, on a one-by-one basis, based upon expected information gain. While myopic test selection is not realistic for many medical applications, non-myopic test selection, in which information gain would be computed for all combinations of variables, would be too demanding. We present three new test-selection algorithms for probabilistic networks, which all employ knowledge-based clusterings of variables; these are a myopic algorithm, a non-myopic algorithm and a semi-myopic algorithm. In a preliminary evaluation study, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.

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