COMBINING BAYESIAN NETWORKS AND ROUGH SETS: FURTHER STEP TOWARDS REASONING ABOUT UNCERTAINTY
DOI:
https://doi.org/10.47839/ijc.7.3.518Keywords:
Bayesian belief networks, rough sets, approximate reasoning, software safety, safety analysisAbstract
This paper discusses a combination of Bayesian belief networks and rough sets for reasoning about uncertainty. The motivation for this work is the problem with assessment of properties of software used in real-time safety-critical systems. A number of authors applied Bayesian networks for this purpose, however, their approach suffers from problems related to calculating the conditional probability distributions, when there is scarcity of experimental data. The current authors propose enhancing this method by using rough sets, which do not require knowledge of probability distributions and thus are helpful in making preliminary evaluations, especially in real-time decision making. The combination of Bayesian network and rough sets tools, Netica and Rosetta, respectively, is used to demonstrate the applicability of this method in a case study of the Australian Navy exercise.References
J. Zalewski, S. Wierzchon, Combining Bayesian Networks and Rough Sets: A New Approach to Reasoning about Uncertainty, Proc. ICNNAI2008, 5th Int’l Conference on Neural Networks and Artificial Intelligence, Minsk, Belarus, May 27-30, 2008, pp. 22-27.
Agena Ltd., Bayesian Net References. Version 4, London, 13 July. 2008, 39 pp., URL: http://agena-risk.com/resources/BN_refs.doc
Z. Pawlak, Rough Sets, International Journal of Computer and Information Sciences, vol. 11, no. 5, pp. 341-356, 1982.
Z. Pawlak, Rough Sets – Theoretical Aspects of Reasoning about Data, Dordrecht: Kluwer Academic Publishers, 1991.
Z. Pawlak, AI and Intelligent Industrial Applications: The Rough Set Perspective, Cybernetics and Systems: An International Journal, vol. 31, pp. 227-252, 2000.
Rough Set Database System – A Bibliographic Database on Wide Aspects of Rough Sets, University of Rzeszow, Poland, April 2008, URL: http://rsds.univ.rzeszow.pl/
J. Grzymala-Busse, Three Approaches to Missing Attribute Values – A Rough Set Perspective. Proc. Workshop on Foundation of Data Mining at the 4th IEEE Int’l Conference on Data Mining, Brighton, UK, November 1-4, 2004.
A. Kornecki, J. Zalewski, Experimental Evaluation of Software Development Tools for Safety-Sritical Real-Time Systems, Innovations in Systems and Software Engineering: A NASA Journal, vol. 1, pp. 176-188, 2005.
J. Zalewski, A.J. Kornecki, H.L. Pfister, Numerical Assessment of Software Development Tools in Real-Time Safety Critical Systems Using Bayesian Belief Networks, Proc. Int’l Multiconference on Computer Science and Information Technology, Wisla, Poland, 6-10, November 2006, pp. 351-360.
M. Neil, N. Fenton, Predicting Software Quality Using Bayesian Belief Networks, in Proc. SEW-21, Annual NASA Goddard Software Engineering Workshop, Washington, DC, 4-5 December 1996, pp. 217-230.
G. Dahll, B.A. Gran, The Use of Bayesian Belief Nets in Safety Assessment of Software Based Systems, International Journal of General Systems, vol. 29, no. 2, pp. 205-229, 2000.
K.A. Delic, F. Mazzanti, L. Strigini, Formalising Engineering Judgement on Software Dependability via Belief Networks, in Proc. DCCA-6, 6th IFIP International Working Conference on Dependable Computing for Critical Applications, M. Dal Cin, C. Meadows, W.H. Sanders, (Eds.), Los Alamitos, CA: IEEE Computer Society, 1998, pp. 291-305.
A. Helminen, Reliability Estimation of Safety-Critical Software-Base Systems Using Bayesian Networks, Report STUK-YTO-TR 178, Radiation and Nuclear Safety Authority, Helsinki, Finland, June 2001.
J. Grzymala-Busse, LERS – A System for Learning from Examples Based on Rough Sets,” in R. Slowinski, ed., Intelligent Decision Support: Handbook of Applications and Advances of Rough Set Theory, Dordrecht: Kluwer, 1992, pp. 3-18.
J. Komorowski, L. Polkowski, A. Skowron, Rough Sets: A Tutorial, in S.K. Pal and A. Skowron, Eds., Rough-Fuzzy Hybridization: A New Method for Decision Making, Berlin: Springer-Verlag, 1998.
Rosetta, The Linnaeus Centre for Bioinformatics, Uppsala Univ., Sweden, URL: http://rosetta.lcb.uu.se/general/download/
Netica, Norsys Software Corporation, Vancouver, BC, Canada, URL: http://www.norsys.com/
B. Das, Representing Uncertainties Using Bayesian Networks, Report No. DSTO-TR-0918, Defence Science and Technology Organization, Information Technology Division Electronics and Surveillance Research Lab, Sydney, Australia, December 1999.
J. Shao, Knowledge Discovery in Alarm Data Analysis. Proc. SOFSEM’96, 23rd Seminar on Current Trends in Theory and Practice of Informatics, Milovy, Czech Republic, November 23-30, 1996, Springer-Verlag, pp. 433-440.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
