B0972
Title: Ensembling imbalanced-spatial-structured support vector machine
Authors: Grace Yi - University of Western Ontario (Canada) [presenting]
Abstract: The support vector machine (SVM) and its extensions have been widely used in various areas. However, these methods cannot effectively handle imbalanced data with spatial association. The ensembling imbalanced-spatial-structured support vector machine (EISS-SVM) method is proposed to handle such data. Not only does the proposed method accommodate the relationship between the response and predictors but also accounts for the spatial correlation existing in data which may be imbalanced. The EISS-SVM classifier embraces the usual SVM as a special case. Numerical studies show the satisfactory performance of the proposed method, and the analysis results are reported for the application of the proposed method to handling the imaging data from ongoing prostate cancer research conducted in Canada.
