Kernel credal classification rule
Khawla El Bendadi, Yissam Lakhdar, El Hassan Sbai · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
In this paper, we propose a kernel version of the credal classification rule (CCR) to perform the classification in a feature space of high dimension. Kernels based approaches have become popular for several years to solve supervised or unsupervised learning problems. In this paper, our method is extended to the CCR. It is realized by replacing the inner product with an appropriate positive definite function, and the corresponding algorithms are called kernel Credal Classification Rule (KCCR). The approach is applied to the classification of the generated and real data to evaluate and compare the performance of the KCCR method with other classification methods.