Gliomas constitute a significant challenge in neurosurgery due to their high incidence and poor prognosis. Despite advancements in tumor detection techniques using machine learning approach with hyperspectral imaging, accurately distinguishing between healthy and tumoral tissues remains challenging. Following this trend, this paper introduces Libra, a low spectral resolution classifier designed for brain tumor detection, leveraging ensemble learning techniques to enhance classification performance. Evaluated across the Helicoid and Slim Brain databases, Libra demonstrates superior tumor sensitivity and a notable increase in accuracy compared to standalone SVM and the state-of-the-art Helicoid classification chain. While facing challenges in accurately distinguishing between blood vessels and tumoral tissues, Libra performs 32 % better than Helicoid in tumor sensitivity and 25.1 % in tumor F1-score. Moreover, when using unseen data, Libra demonstrates notable improvements of 7.8 % and 6.9 % respectively. These improvements are obtained using low complexity algorithms exploiting ensemble learning techniques.