Determine the Number of Unknown Targets in Open World Based on Elbow Method

Fan Liu · Yong Deng

334 citations0/0 files storedIEEE Transactions on Fuzzy Systems

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Multi-Criteria Decision Making Advanced Clustering Algorithms Research Rough Sets and Fuzzy Logic

Abstract

Generalized evidence theory as an extension of Dempster-Shafer evidence theory can deal with uncertain information fusion in the open world. However, one of the open issues is to detect the number of unknown targets. In this article, a new method based on the elbow method is proposed to solve this problem. After the identification of the open world, K-means clustering is used to cluster categories. Then, the elbow method is used to find a correct number of unknown targets. The frame of discernment is reupdated. To test effectiveness of the proposed method, several experiments are conducted. The results illustrate the advantage of the proposed method in the open world.

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References

1953Who Belongs in the Family?2000Combining belief functions when evidence conflicts 1995A k-nearest neighbor classification rule based on Dempster-Shafer theory 2008A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory 2013Evidential reasoning rule for evidence combination 2018Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy 2014Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making 2000A neural network classifier based on Dempster-Shafer theory

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