Rule base generation from numerical data has been a dynamic research topic within the fuzzy community in the last decades, and several well-established methods have been proposed. While some authors presented simple, empirical approaches, but which generally show high error rates, others turned to complex heuristic techniques to improve accuracy. In this paper, an extension of the classical Wang-Mendel method is proposed. While keeping a linear complexity, the new method achieves performances close to those of more complex methods based on cooperative rules (COR). Results on synthetic data show the potential of the proposed method as a complexity-accuracy trade-off.