A clustering based method to complete frame of discernment |
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Institution: | 1. Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu 610054, China;2. Key Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Nanjing 210096, China;3. School of Education, Shannxi Normal University, Xi''an 710062, China;4. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1211, Japan;5. Department of Management, Technology, and Economics, ETH Zurich, Zurich 8092, Switzerland |
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Abstract: | When the existing information does not contain all categories, the Generalized Evidence Theory (GET) can deal with information fusion. However, the question of how to determine the number of categories through GET is still intriguing. To address this question, a modified k-means clustering, named centers initialized clustering is proposed, filling the gap of identification and complement of the frame of discernment. Based on this clustering method, the number of categories is determined. The initialized centers selected by center density keep the cluster results constant, enhancing the stability of clustering results. Besides, constructing Generalized basic Probability Assignment (GBPA) modules in a conservative way improves the reliability of the results. The mass of empty set in combined GBPAs is the indicator of the number of categories. Experiments on real and artificial data sets are conducted to show the effectiveness. |
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Keywords: | Dempster-Shafer evidence theory Generalized evidence theory Information fusion K-means clustering Open world assumption Target recognition |
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