Improving Intelligent Decision Making in Urban Planning: Using Machine Learning Algorithms
Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support th...
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| Published in | International journal of business analytics Vol. 8; no. 3; pp. 40 - 58 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Montclair
IGI Global
01.07.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2334-4547 2334-4555 |
| DOI | 10.4018/IJBAN.2021070104 |
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| Abstract | Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support the decision process. This paper aims to elaborate an intelligent decision support system (IDSS) that provides relevant assistance to urban planners in urban projects. This research addresses the use of new techniques that contribute to intelligent decision making: machine learning classifiers, naïve Bayes classifier, and agglomerative clustering. Finally, a prototype is being developed to concretize the proposition. |
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| AbstractList | Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support the decision process. This paper aims to elaborate an intelligent decision support system (IDSS) that provides relevant assistance to urban planners in urban projects. This research addresses the use of new techniques that contribute to intelligent decision making: machine learning classifiers, naïve Bayes classifier, and agglomerative clustering. Finally, a prototype is being developed to concretize the proposition. |
| Audience | Academic |
| Author | Eom, Sean B Khediri, Abderrazak Laouar, Mohamed Ridda |
| AuthorAffiliation | Southeast Missouri State University, USA Laboratory of Mathematics, Informatics, and Systems (LAMIS), University of Larbi Tebessi, Tebessa, Algeria |
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| SubjectTerms | Algorithms Business analytics Data mining Decision-making Machine learning Multi-agent systems |
| Title | Improving Intelligent Decision Making in Urban Planning: Using Machine Learning Algorithms |
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