Qualitative uncertainty models from random set theory

Olaf Wolkenhauer

doi:10.1007/bfb0052875

Qualitative uncertainty models from random set theory

Olaf Wolkenhauer^*

^*Corresponding author for this work

University of Manchester

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

2 Scopus citations

Abstract

When only incomplete information about the probability distribution of an experiment is available, we may have to admit imprecision in the formulation of an uncertainty model. In this paper Random Set Theory is used to build possibilistic uncertainty models from sampled data. In particular Goodman's one-point coverage function of a class of random sets is estimated from data. Finally, we focus on an example to illustrate how from random sets induced possibility distributions may be used in the detection of changes in time-series data.

Original language	English
Title of host publication	Advances in Intelligent Data Analysis
Subtitle of host publication	Reasoning about Data - 2nd International Symposium, IDA-1997, Proceedings
Editors	Xiaohui Liu, Paul Cohen, Michael Berthold
Publisher	Springer
Pages	609-620
Number of pages	12
ISBN (Print)	9783540633464
DOIs	https://doi.org/10.1007/bfb0052875
State	Published - 1997
Externally published	Yes
Event	2nd International Symposium on Intelligent Data Analysis, IDA 1997 - London, United Kingdom Duration: 4 Aug 1997 → 6 Aug 1997

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1280
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Symposium on Intelligent Data Analysis, IDA 1997
Country/Territory	United Kingdom
City	London
Period	4/08/97 → 6/08/97

Access to Document

10.1007/bfb0052875

Cite this

Wolkenhauer, O. (1997). Qualitative uncertainty models from random set theory. In X. Liu, P. Cohen, & M. Berthold (Eds.), Advances in Intelligent Data Analysis: Reasoning about Data - 2nd International Symposium, IDA-1997, Proceedings (pp. 609-620). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1280). Springer. https://doi.org/10.1007/bfb0052875

Wolkenhauer, Olaf. / Qualitative uncertainty models from random set theory. Advances in Intelligent Data Analysis: Reasoning about Data - 2nd International Symposium, IDA-1997, Proceedings. editor / Xiaohui Liu ; Paul Cohen ; Michael Berthold. Springer, 1997. pp. 609-620 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0a1e446a7384448585f5ec4f199df0db,

title = "Qualitative uncertainty models from random set theory",

abstract = "When only incomplete information about the probability distribution of an experiment is available, we may have to admit imprecision in the formulation of an uncertainty model. In this paper Random Set Theory is used to build possibilistic uncertainty models from sampled data. In particular Goodman's one-point coverage function of a class of random sets is estimated from data. Finally, we focus on an example to illustrate how from random sets induced possibility distributions may be used in the detection of changes in time-series data.",

author = "Olaf Wolkenhauer",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1997.; 2nd International Symposium on Intelligent Data Analysis, IDA 1997 ; Conference date: 04-08-1997 Through 06-08-1997",

year = "1997",

doi = "10.1007/bfb0052875",

language = "English",

isbn = "9783540633464",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "609--620",

editor = "Xiaohui Liu and Paul Cohen and Michael Berthold",

booktitle = "Advances in Intelligent Data Analysis",

}

Wolkenhauer, O 1997, Qualitative uncertainty models from random set theory. in X Liu, P Cohen & M Berthold (eds), Advances in Intelligent Data Analysis: Reasoning about Data - 2nd International Symposium, IDA-1997, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1280, Springer, pp. 609-620, 2nd International Symposium on Intelligent Data Analysis, IDA 1997, London, United Kingdom, 4/08/97. https://doi.org/10.1007/bfb0052875

Qualitative uncertainty models from random set theory. / Wolkenhauer, Olaf.
Advances in Intelligent Data Analysis: Reasoning about Data - 2nd International Symposium, IDA-1997, Proceedings. ed. / Xiaohui Liu; Paul Cohen; Michael Berthold. Springer, 1997. p. 609-620 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1280).

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

TY - GEN

T1 - Qualitative uncertainty models from random set theory

AU - Wolkenhauer, Olaf

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 1997.

PY - 1997

Y1 - 1997

N2 - When only incomplete information about the probability distribution of an experiment is available, we may have to admit imprecision in the formulation of an uncertainty model. In this paper Random Set Theory is used to build possibilistic uncertainty models from sampled data. In particular Goodman's one-point coverage function of a class of random sets is estimated from data. Finally, we focus on an example to illustrate how from random sets induced possibility distributions may be used in the detection of changes in time-series data.

AB - When only incomplete information about the probability distribution of an experiment is available, we may have to admit imprecision in the formulation of an uncertainty model. In this paper Random Set Theory is used to build possibilistic uncertainty models from sampled data. In particular Goodman's one-point coverage function of a class of random sets is estimated from data. Finally, we focus on an example to illustrate how from random sets induced possibility distributions may be used in the detection of changes in time-series data.

UR - https://www.scopus.com/pages/publications/79959701489

U2 - 10.1007/bfb0052875

DO - 10.1007/bfb0052875

M3 - Conference contribution

AN - SCOPUS:79959701489

SN - 9783540633464

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 609

EP - 620

BT - Advances in Intelligent Data Analysis

A2 - Liu, Xiaohui

A2 - Cohen, Paul

A2 - Berthold, Michael

PB - Springer

T2 - 2nd International Symposium on Intelligent Data Analysis, IDA 1997

Y2 - 4 August 1997 through 6 August 1997

ER -

Wolkenhauer O. Qualitative uncertainty models from random set theory. In Liu X, Cohen P, Berthold M, editors, Advances in Intelligent Data Analysis: Reasoning about Data - 2nd International Symposium, IDA-1997, Proceedings. Springer. 1997. p. 609-620. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/bfb0052875

Qualitative uncertainty models from random set theory

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this