关键词: 分类, 高斯核, 尺度空间, 凸壳, 非平衡数据集

Abstract: The expansion of feature points of the linear scale space is transformed into the classification of multi-scale data set within the same scale, which belongs to the classification of scale invariant non-equilibrium. This paper presents a sample approach based on scale space with Gaussian kernel learning to solve classification on imbalance dataset by support vector machine (SVM). The method first preprocesses the data by over-sampling the minority class in kernel space, then finds the pre-images of the synthetic samples based on a distance relation between kernel space and input space, finally appends these pre-images to the original dataset to train. As a result, the inconsistency which is brought about by processing samples in different spaces is overcome. The sampling strategies of the method not only can decrease imbalanced rate of training dataset, but also can enlarge convex hull of the minority class. Consequently, the problem of boundary skew can be amended more effectively. Experimental results on real dataset indicate that the generalization performance of the result classifier is improved and the algorithm can work well on expanding the feature points stably for a certain scale.

Key words: classification, Gaussian kernel, scale space, convex hull, imbalanced datasets