Enhanced risk management by an emerging multi-agent architecture

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題名:	Enhanced risk management by an emerging multi-agent architecture
作者:	Lin, Sin-Jin Hsu, Ming-Fu
貢獻者:	會計系
關鍵詞:	imbalanced dataset multi-agent learning risk management decision-making
日期:	2014-09
上傳時間:	2015-01-20 15:55:52 (UTC+8)
摘要:	Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.
關聯:	CONNECTION SCIENCE 卷: 26 期: 3 頁碼: 245-259
顯示於類別:	[會計學系暨研究所 ] 期刊論文

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