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Title: Data adaptive support vector machine with imbalanced observations Authors:  Wenqing He - University of Western Ontario (Canada) [presenting]
Abstract: Support vector machines (SVM) have been widely used as classifiers in various settings. However, such methods are faced with newly emerging challenges such as imbalanced observations and noise data. We will present an SVM method using a data-adaptive kernel to feature imbalanced observations by considering the location of support vectors in the feature space and thereby generates more accurate classification results. The performance of the proposed method is compared with existing methods using numerical studies and illustrated through a prostate cancer image example.