Classifier fusion is established as an effective methodology for boosting performance in different settings and one-class classification is no exception. In this study, we consider the one-class classifier fusion problem by modelling the sparsity/uniformity of the ensemble. To this end, we formulate a convex objective function to learn the weights in a linear ensemble model and impose a variable Lp-norm constraint on the weight vector. The vector-norm constraint enables the model to adapt to the intrinsic uniformity/sparsity of the ensemble in the space of base learners and acts as a (soft) classifier selection mechanism by shaping the relative magnitudes of fusion weights. Drawing on the Frank-Wolfe algorithm, we then present an effective approach to solve the formulated convex constrained optimisation problem efficiently. We evaluate the proposed one-class classifier combination approach on multiple data sets from diverse application domains and illustrate its merits in comparison to the existing approaches.

翻译：分类器融合已被证明是在不同场景下提升性能的有效方法，单类分类也不例外。在本研究中，我们通过建模集成学习器的稀疏性/均匀性来考虑单类分类器融合问题。为此，我们构建了一个凸目标函数来学习线性集成模型中的权重，并对权重向量施加可变Lp-范数约束。向量范数约束使模型能够适应基学习器空间中集成学习器固有的均匀性/稀疏性，并通过调整融合权重的相对大小，起到（软性）分类器选择机制的作用。基于弗兰克-沃尔夫算法，我们提出了一种有效方法，高效求解所构建的凸约束优化问题。我们在来自不同应用领域的多个数据集上评估了所提出的单类分类器组合方法，并展示了其相较于现有方法的优势。