Efficient AdaBoost Region Classification

摘要

The task of region classification is to construct class regions containing the correct classes of the objects being classified with an error probability ε ∈ [0,1]. To turn a point classifier into a region classifier, the conformal framework is employed [11,14]. However, to apply the framework we need to design a non-conformity function. This function has to estimate the instance's non-conformity for the point classifier used.rnThis paper introduces a new non-conformity function for AdaBoost. The function has two main advantages over the only existing non-conformity function for AdaBoost. First, it reduces the time complexity of computing class regions with a factor equal to the size of the training data. Second, it results in statistically better class regions.