Color constancy (CC) refers to the ability to reproduce the veritable color of objects by eliminating the influence of environmental illumination. The performance of CC has been greatly boosted by advanced learning-based methods, such as deep neural networks. However, most current deep learning methods suffer from large model sizes and large-scale training datasets for promising performance. We propose least square support vector machine-CC (LSSVM-CC), an efficient CC method using LSSVM, which combines preferably unitary statistics-based methods to achieve better illuminant estimation performance at low computation efforts with small training samples. The core of our LSSVM-CC is to cluster training images based on illumination-preferred features and then train an ensemble model using LSSVM for each cluster, which can effectively improve the overall performance. Once trained, our LSSVM-CC selects adaptive models for different input images to perform accurate illuminant estimation in the testing phase. Experiments on typical benchmark datasets show that our method significantly outperforms most traditional statistics-based methods and is also comparable to some sophisticated and computationally demanding learning-based methods.