This study presents a new machine learning (support vector machine (SVM))-based cooperative spectrum sensing (CSS) model, which utilises the methods of user grouping, to reduce cooperation overhead and effectively improve detection performance. Cognitive radio users were properly grouped before the cooperative sensing process using energy data samples and an SVM model. The resulting user group which participates in cooperative sensing procedures is safe, less redundant, or the optimised user group. Three grouping algorithms are presented in this study. The first grouping algorithm divides normal and abnormal users (malicious and severely fading users) into two groups. The second grouping algorithm distinguishes redundant and non-redundant users. The third grouping algorithm establishes an optimisation model with the objective of minimising average correlation within subsets. All users are then divided into a specific number of optimised groups, only one of which is required for cooperative sensing in each time. The performances of the three algorithms were quantified in terms of the average training time, classification speed and classification accuracy. Experimental results showed the proposed algorithms achieved their intended function and outperformed a conventional machine learning-based CSS model (proposed by Karaputugala et al.) in terms of security, energy consumption, and sensing efficiency.