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Updating incomplete framework of target recognition database based on fuzzy gap statistic

2021; Elsevier BV; Volume: 107; Linguagem: Inglês

10.1016/j.engappai.2021.104521

1873-6769

Zichong Chen, Rui Cai,

Remote-Sensing Image Classification

Generalized evidence theory (GET) is a generalization of Dempster–Shafer evidence theory. It copes with information in an open world, which makes up for the shortcoming that Dempster–Shafer evidence theory cannot handle information conflict effectively. However, GET also faces an unavoidable problem: how to determine the number of unknown targets in the incomplete frame of discernment (FOD). Fuzzy C-means (FCM) is a clustering algorithm that divides the original data set into different clusters and summarizes similar data into the same cluster. Therefore, determining the number of unknown targets in the open world can be transformed into finding the number of clusters. However, FCM has the disadvantage of subjectively controlling the number of clusters. In order to overcome this shortcoming, we use fuzzy gap statistic algorithm (FGS) to optimize it. FGS can effectively determine the optimal number of clusters in FCM. Therefore, this paper proposes a new method based on FGS to determine the number of unknown targets in the open world. In addition, to verify the method's accuracy, we conducted seven experiments based on the University of California Irvine (UCI) data sets, including Iris, glass, Haberman, Knowledge, Robot, seeds, and WDBC. Finally, the experimental results illustrate that the proposed method to determine the number of unknown targets in the incomplete FOD has high effectiveness.