Abstract：To improve the generalization ability of maritime surveillance radars in complicatedly interferential environment, a dynamic ensemble selection algorithm based on k-medoids clustering and random reference classifier(KMRRC) is proposed. Firstly, a pool of base classifiers are generated through Bagging technique. Secondly, k-medoids clustering is used to divided all the base classifiers into several clusters based on pairwise diversity metric. Then, the RRC model for each base classifier is constructed on the basis of validation dataset. Finally, the RRC model is employed to select some of the most competent classifiers from each cluster for ensemble and decision making. The parameters of KMRRC are determined by optimization experiment based on the self-built high resolution range profile(HRRP) dataset, and the performance of KMRRC is compared with nine ensemble methods and the base classification algorithm using the HRRP dataset and other seventeen UCI datasets in Java environment with a Weka stand-alone library. Besides, the influence of the diversity measures on the performance of KMRRC is further studied. The feasibility of KMMRRC in the field of target recognition for maritime surveillance radars is verified by experiments.
范学满，胡生亮，贺静波. 基于聚类和随机参考分类器的对海雷达目标识别算法*[J]. 模式识别与人工智能, 2017, 30(11): 983-994.
FAN Xueman, HU Shengliang, HE Jingbo. Target Recognition Algorithm for Maritime Surveillance Radars Based on Clustering and Random Reference Classifier. , 2017, 30(11): 983-994.
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