Exploiting Ontological Reasoning in Argumentation Based Multi-agent Collaborative Classification

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• 作者：Zhiyong Hao (7)
  Bin Liu (7)
  Junfeng Wu (7)
  Jinhao Yao (7)
  
  7. National University of Defense Technology ; Changsha ; 410073 ; Hunan ; People鈥檚 Republic of China
  
• 关键词：Argumentation ; Prism algorithm ; Collaborative classification ; Domain ontology
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9011
• 期：1
• 页码：23-33
• 全文大小：242 KB
• 参考文献：1. Cendrowska, J (1987) PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies 27: pp. 349-370 CrossRef
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  13. Yao, L., et al.: Evaluating the valuable rules from different experience using multiparty argument games. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, vol. 02. IEEE Computer Society (2012)
  14. Zhang, J., Honavar, V.: Learning decision tree classifiers from attribute value taxonomies and partially specified data. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03) (2003)
  15. Zhu, F.-Y., Qin, S.-Y.: Small-shaped space target recognition based on wavelet decomposition and support vector machine. International Conference on Wavelet Analysis and Pattern Recognition, vol. 3. IEEE (2007)
  
• 作者单位：Intelligent Information and Database Systems
• 丛书名：978-3-319-15701-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Argumentation-based multi-agent collaborative classification is a promising paradigm for reaching agreements in distributed environments. In this paper, we advance the research by introducing a new domain ontology enriched inductive learning approach for collaborative classification, in which agents are able to constructing arguments taking into account their own domain knowledge. This paper focuses on classification rules inductive learning, and presents Arguing SATE-Prism, a domain ontology enriched approach for multi-agent collaborative classification based on argumentation. Domain ontology, in this context, is exploited for driving a paradigm shift from traditional data-centered hidden pattern mining to domain-driven actionable knowledge discovery. Preliminary experimental results show that higher classification accuracy can be achieved by exploiting ontological reasoning in argumentation based multi-agent collaborative classification. Our experiments also demonstrate that the proposed approach out-performs comparable classification paradigms in presence of instances with missing values, harnessing the advantages offered by ontological reasoning.