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发表时间:2018-10-08 阅读次数:386次
报告题目: Data Adaptive Support Vector Machine with Application to Prostate Cancer Imaging Data
报 告 人:Wenqing He
报告人所在单位:Department of Statistical and Actuarial Sciences University of Western Ontario, Canada
报告日期:2018-10-08 星期一
报告时间:15:00-16:00
报告地点: Room 106, Shanghai Center for Mathematical Sciences
  
报告摘要:

Support vector machines (SVM) have been widely used as classifiers in various settings including pattern recognition, texture mining and image retrieval. However, such methods are faced with newly emerging challenges such as imbalanced observations and noise data. In this talk, I will discuss the impact of noise data and imbalanced observations on SVM classification and present a new data adaptive SVM classification method.

This work is motivated by a prostate cancer imaging study conducted in London Health Science Center. A primary objective of this study is to improve prostate cancer diagnosis and thereby to guide the treatment based on statistical predictive models. The prostate imaging data, however, are quite imbalanced in that the majority voxels are cancer-free while only a very small portion of voxels are cancerous. This issue makes the available SVM classifiers typically skew to one class and thus generate invalid results. Our proposed SVM method uses a data adaptive kernel to reflect the feature of imbalanced observations; the proposed method takes into consideration of 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.

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本年度学院报告总序号:218

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