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%.
Highlights
Data mining (DM) is the process of analyzing data, discovering data patterns, and predicting future trends based on previously analyzed information
Proposed Work e proposed work aims to develop a collaborative classification model using an appropriate multiagent system (MAS). e system proposed in this study focuses on cancer hospitals worldwide
® ® using open-source tools IBM SPSS Modeler software by integrating it with Python to use Smart Python multiagent development environment (SPADE) that is a framework for the development, execution, and management of MAS in distributed computer environments
Summary
Data mining (DM) is the process of analyzing data, discovering data patterns, and predicting future trends based on previously analyzed information. Mathematical Problems in Engineering e MAS is suitable for distributed problem solving and enables the creation of goal-oriented autonomous agents that operate in shared environments with communication and coordination capabilities. Such system can be defined as a collection of agents with their own problem-solving capabilities that can interact to reach an overall goal [10]. Multiple data acquisition parties that are not transferred into centralized data warehouses are present due to patient privacy and possibility of exposure [16] For this reason, a multiagent system (MAS) for mutual collaboration classification is proposed in this study. The proposed system for solving the collaborative classification task is established on the basis of MAS, DM, and DDM.
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