Xin (Shane) Liu

Data-adaptive Kernel Support Vector Machine

The support vector machine (SVM) is popularly used as a classifier in applications such as pattern recognition, texture mining and image retrieval owing to its flexibility and interpretability. However, its performance deteriorates when the response classes are imbalanced. To enhance the performance of the support vector machine classifier in the imbalanced cases we investigate a new two stage method by adaptively scaling the kernel function. Based on the information obtained from the standard SVM in the first stage, we conformally rescale the kernel function in a data adaptive fashion in the second stage so that the separation between two classes can be effectively enlarged especially when observations are imbalanced. The proposed method takes into account the location of the support vectors in the feature space, therefore is especially appealing when the response classes are imbalanced. Simultaneously, we consider how to select the important features with data-adaptive kernels in SVMs, and spatial association that may exist. The approach is further applied in multi-category classification problems.