A dynamic selection ensemble method for target recognition based on clustering and randomized reference classifier

In order to improve the generalization ability and recognition efficiency of the maritime surveillance radar, a novel selection ensemble technique, termed KMRRC, based on k-medoids clustering and random reference classifier (RRC) is proposed. By disturbing the training set base classifiers are generated, which are then divided into several clusters based on pairwise diversity metrics, finally the RRC model is used to select several most competent classifiers from each cluster to classify each query object. The performance of KMRRC is compared against nine ensemble learning methods using a self-built high range resolution profile (HRRP) data set and twenty UCI databases. The experimental results clearly show the KMMRRC's feasibility and effectiveness. In addition, the influence of the selection of diversity measures is studied concurrently.