An Intelligent Decision Support System Based on Multi Agent Systems for Business Classification Problem

Mais Haj Qasem, Mohammad Aljaidi (), Ghassan Samara, Raed Alazaidah, Ayoub Alsarhan and Mohammed Alshammari
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Mais Haj Qasem: Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan
Mohammad Aljaidi: Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan
Ghassan Samara: Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan
Raed Alazaidah: Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan
Ayoub Alsarhan: Department of Information Technology, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, Zarqa 13116, Jordan
Mohammed Alshammari: Faculty of Computing and Information Technology, Northern Border University, Rafha 91431, Saudi Arabia

Sustainability, 2023, vol. 15, issue 14, 1-14

Abstract: The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system.

Keywords: intelligent decision support systems (IDSS); classification; FIPA standards; multi-agent system; Naïve Bayesian (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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