Exclusivity Regularized Machine
This work addresses the need for better ensemble methods in machine learning, though it appears incremental by building on existing diversity concepts.
The paper tackled the problem of improving ensemble learning by defining a novel diversity measure called exclusivity and proposing the Exclusivity Regularized Machine (ERM) to jointly minimize training error and enhance diversity, with experiments showing superiority in accuracy and efficiency over state-of-the-art methods.
It has been recognized that the diversity of base learners is of utmost importance to a good ensemble. This paper defines a novel measurement of diversity, termed as exclusivity. With the designed exclusivity, we further propose an ensemble model, namely Exclusivity Regularized Machine (ERM), to jointly suppress the training error of ensemble and enhance the diversity between bases. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to effectively and efficiently seek the optimal solution of ERM. Theoretical analysis on convergence and global optimality of the proposed algorithm, as well as experiments are provided to reveal the efficacy of our method and show its superiority over state-of-the-art alternatives in terms of accuracy and efficiency.