LibD3C: ensemble classifiers with a clustering and dynamic selection strategy. Selective ensemble is a learning paradigm that follows an “overproduce and choose” strategy, where a number of candidate classifiers are trained, and a set of several classifiers that are accurate and diverse are selected to solve a problem. In this paper, the hybrid approach called D3C is presented; this approach is a hybrid model of ensemble pruning that is based on k-means clustering and the framework of dynamic selection and circulating in combination with a sequential search method. Additionally, a multi-label D3C is derived from D3C through employing a problem transformation for multi-label classification. Empirical study shows that D3C exhibits competitive performance against other high-performance methods, and experiments in multi-label datasets verify the feasibility of multi-label D3C.