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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.
Qasem et al. (Thu,) studied this question.