Multiagent System for Mutual Collaboration Classification for Cancer Detection

Abstract

A multiagent system (MAS) is a mechanism for creating goal-oriented autonomous agents in shared environments with communication and coordination facilities. Distributed data mining benefits from this goal-oriented mechanism by implementing various distributed clustering, classification, and prediction techniques. Hence, this study developed a novel multiagent model for distributed classification tasks in cancer detection with the collaboration of several hospitals worldwide using different classifier algorithms. A hospital agent requests help from other agents for instances that are difficult to classify locally. The agents communicate their beliefs (calculated classification), and others decide on the benefit of using such beliefs in classifying instances and adjusting their prior assumptions on each class of data. A MAS model state and behavior and communication are then developed to facilitate information sharing among agents. Regarding accuracy, implementing the proposed approach in comparison with typically different noncommunicated distributed classifications shows that sharable information considerably increases the classification task accuracy by 25.77%.

Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

“…The second phase describes the Bayesian classification algorithm that each agent applies to build their model and explains the feedback processing that to help the initiator decide whether to accept the received class label from other agents, whereas the MAS protocols that agents use in their interactions and communications are described in the third phase. This paper is extended form our work [37] which showed example for our proposed work.…”

“…In this phase MAS-DDM-NB is implemented by using that same MAS Protocol that we used in our pervious paper that found in [37]. Accordingly, the resulting data are distributed among the agents, and each agent is given training and testing sets.…”

A Multiagent Game Theoretic Simulation of Public Policy Coordination through Collaboration

“…According to [36], multiagent systems are preferable to simple-agent-based models because they offer "a more emergent view of macroeconomic quantities". Multiagent modeling has already been used to simulate human and human-like behavior successfully in the fields of health care, education, decision systems [12,35,37], and engineering [15,38,39]. However, there has been no attempt in the economic literature to simulate policy coordination through collaboration using multiagent systems, so the multiagent simulation in this paper offers the first step in understanding the different approaches to policy coordination.…”

“…In general, public policy coordination involves attempts to avoid conflicts between the decisions of different government agencies, as well as aligning such decisions and actions to produce solutions that are of mutual benefit to all [12]. Coordination can therefore be approached from the perspective of cooperation as a way to manage conflict or from the perspective of collaboration that is defined as a type of decision-making in which agents adjust their strategies for mutual benefit [13].…”