A generalized approach to construct node probability table for Bayesian belief network using fuzzy logic
The cause–effect relationship has tremendous role in interpreting the engineering and scientific problems which basically deals with the identifying potential causes of problem. Bayesian belief networks (BBN) also referred as Bayesian casual probabilistic network used widely to deal with probabilist...
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| Published in | The Journal of supercomputing Vol. 80; no. 1; pp. 75 - 97 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0920-8542 1573-0484 |
| DOI | 10.1007/s11227-023-05458-y |
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| Abstract | The cause–effect relationship has tremendous role in interpreting the engineering and scientific problems which basically deals with the identifying potential causes of problem. Bayesian belief networks (BBN) also referred as Bayesian casual probabilistic network used widely to deal with probabilistic events to elucidate the complications having uncertainty. A major challenge in BBN is to construct a node probability table (NPT), which grows exponentially with the rising number of variables. Various approaches exist for NPT construction, including expert elicitation, data analysis, survey and weighted functions, noisy-OR, noisy-MAX, recursive noisy-OR (ROR), extended recursive noisy-OR, and ranked nodes. However, these methods are problem-specific and lacking behind a generalized approach applicable to all problem types. To address this issue, this paper proposes a generalized universal approach for constructing the NPT using fuzzy logic. The suggested strategy has been validated by applying it to a BBN prototype for software design and development. The proposed strategy has been evaluated with best-case and worst-case software metrics. |
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| AbstractList | The cause–effect relationship has tremendous role in interpreting the engineering and scientific problems which basically deals with the identifying potential causes of problem. Bayesian belief networks (BBN) also referred as Bayesian casual probabilistic network used widely to deal with probabilistic events to elucidate the complications having uncertainty. A major challenge in BBN is to construct a node probability table (NPT), which grows exponentially with the rising number of variables. Various approaches exist for NPT construction, including expert elicitation, data analysis, survey and weighted functions, noisy-OR, noisy-MAX, recursive noisy-OR (ROR), extended recursive noisy-OR, and ranked nodes. However, these methods are problem-specific and lacking behind a generalized approach applicable to all problem types. To address this issue, this paper proposes a generalized universal approach for constructing the NPT using fuzzy logic. The suggested strategy has been validated by applying it to a BBN prototype for software design and development. The proposed strategy has been evaluated with best-case and worst-case software metrics. |
| Author | Yadav, Dilip Kumar Jha, Sudhanshu Kumar Prakash, Shiv Prasad, Mukesh Kumar, Chandan |
| Author_xml | – sequence: 1 givenname: Chandan surname: Kumar fullname: Kumar, Chandan organization: School of Computing, Amrita Vishwa Vidyapeetham – sequence: 2 givenname: Sudhanshu Kumar surname: Jha fullname: Jha, Sudhanshu Kumar email: sudhanshukumarjha@gmail.com organization: Department of Electronics and Communication, University of Allahabad – sequence: 3 givenname: Dilip Kumar surname: Yadav fullname: Yadav, Dilip Kumar organization: Department of Computer Science and Engineering, NIT Jamshedpur – sequence: 4 givenname: Shiv surname: Prakash fullname: Prakash, Shiv organization: Department of Electronics and Communication, University of Allahabad – sequence: 5 givenname: Mukesh surname: Prasad fullname: Prasad, Mukesh organization: Faculty of Engineering and Information Technology, Australian Artificial Intelligence Institute, University of Technology, Sydney |
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| Cites_doi | 10.1016/j.ijar.2021.09.013 10.1016/0004-3702(90)90060-D 10.1214/088342304000000189 10.1016/S0167-9236(03)00095-2 10.1002/int.10080 10.1016/j.ress.2019.02.001 10.1007/s13198-011-0078-1 10.1016/j.ijar.2011.01.008 10.1109/32.815326 10.1109/TKDE.2016.2535229 10.1007/s13198-014-0326-2 10.1109/TSMCB.2004.834424 10.1016/j.dss.2012.11.001 10.1007/s13198-014-0325-3 10.1109/TKDE.2007.1073 10.1049/sfw2.12043 10.1007/s10664-012-9218-8 10.1002/9780470684023 10.1109/TSE.2007.70722 10.1016/j.ijar.2014.02.008 10.1007/s10664-008-9072-x 10.1109/FUZZ.2003.1206547 10.1109/BRICS-CCI-CBIC.2013.39 10.1002/0470033312 10.1109/C3IT.2015.7060187 10.1016/B978-0-08-051489-5.50008-4 10.1142/9789814261302_0039 10.1061/(ASCE)0887-3801(2007)21:4(265) 10.1080/03081079.2022.2086541 10.3389/feart.2021.674618 |
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| Keywords | Fuzzy logic Node probability table (NPT) Software metrics Bayesian belief network (BBN) |
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| References | Kumar, Yadav (CR14) 2017; 8 CR16 Santos, Wilkinson, Santos (CR29) 2011; 52 CR13 Ross (CR28) 2009 CR34 CR33 Cooper (CR4) 1990; 42 CR10 CR30 Okutan, Yıldız (CR23) 2014; 19 Bellhouse (CR3) 2004; 19 Abraham de Sousa, de Souza, Reis (CR1) 2022; 16 Díez, Galán (CR5) 2003; 18 Quintanar-Gago, Nelson (CR27) 2021; 139 Yadav, Yadav (CR31) 2017; 8 Mohanta, Vinod, Ghosh, Mall (CR18) 2011; 2 CR2 Pai, Dugan (CR24) 2007; 33 Fenton, Neil (CR6) 1999; 25 CR9 CR26 CR25 Yong, Xiangzhou, Ngai, Ruichu, Mei (CR32) 2013; 56 Laitila, Virtanen (CR15) 2016; 28 CR22 CR21 Zhou, Fenton, Neil (CR35) 2014; 55 CR20 Fenton, Neil, Caballero (CR7) 2007; 19 Fenton, Neil, Marsh, Hearty, Radliński, Krause (CR8) 2008; 13 Kayu, David, Simon (CR11) 2019; 186 Koski, Noble (CR12) 2009 Lemmer, Gossink (CR17) 2004; 34 Nadkarni, Shenoy (CR19) 2004; 38 T Kayu (5458_CR11) 2019; 186 NE Fenton (5458_CR7) 2007; 19 JF Lemmer (5458_CR17) 2004; 34 S Nadkarni (5458_CR19) 2004; 38 FJ Díez (5458_CR5) 2003; 18 5458_CR16 P Laitila (5458_CR15) 2016; 28 5458_CR33 HB Yadav (5458_CR31) 2017; 8 5458_CR34 5458_CR13 E Santos Jr (5458_CR29) 2011; 52 DA Quintanar-Gago (5458_CR27) 2021; 139 TJ Ross (5458_CR28) 2009 5458_CR30 5458_CR10 GJ Pai (5458_CR24) 2007; 33 LR Abraham de Sousa (5458_CR1) 2022; 16 A Okutan (5458_CR23) 2014; 19 5458_CR26 GF Cooper (5458_CR4) 1990; 42 5458_CR22 N Fenton (5458_CR8) 2008; 13 5458_CR2 NE Fenton (5458_CR6) 1999; 25 5458_CR25 DR Bellhouse (5458_CR3) 2004; 19 T Koski (5458_CR12) 2009 S Mohanta (5458_CR18) 2011; 2 5458_CR20 C Kumar (5458_CR14) 2017; 8 5458_CR21 H Yong (5458_CR32) 2013; 56 5458_CR9 Y Zhou (5458_CR35) 2014; 55 |
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| Title | A generalized approach to construct node probability table for Bayesian belief network using fuzzy logic |
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