学术报告一：Data Adaptive Support Vector Machine with Application to Prostate Cancer Imaging Data
报告人：Wenqing He（Professor，Department of Statistical and Actuarial Sciences University of Western Ontario, Canada）
时 间：2018年9月20日 上午10:00-11:00
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.
Wenqing He，男，博士，教授，博士生导师。2002年加拿大滑铁卢大学统计精算系获博士学位。现任滑铁卢大学统计精算系终身教授。Wenqing He教授长期从事统计的理论和应用的研究工作，其研究领域涉及生存分析, 高维数据分析，统计学习，统计计算等。先后在国际统计学top期刊《The Journal of the Royal Statistical Society, SeriesB》，生物信息top期刊《Bioinformatics》, 以及一些著名期刊《Biometrics》，《Statistica Sinica》，《Technometrics》，《Statistics in Medicines》等国际权威刊物上发表论文六十余篇。
学术报告二：Making Sense of Noisy Data: Some Issues and Methods
报告人：Grace Y. Yi （Professor，Department of Statistical and Actuarial Sciences University of Western Ontario, Canada）
时 间：2018年9月20日 上午11:00-12:00
摘要：Thanks to the advancement of modern technology in acquiring data, massive data with diversefeatures and big volume are becoming more accessible than ever. The impact of big data is significant.While the abundant volume of data presents great opportunities for researchers to extract useful information for new knowledge gain and sensible decision making, big data present great challenges.A very important, sometimes overlooked challenge is the quality and provenance of the data. Big data are not automatically useful; big data are often raw and involve considerable noise. Typically, the challenges presented by noisy data with measurement error, missing observations and high dimensionality are particularly intriguing. Noisy data with these features arise ubiquitously from various fields including health sciences, epidemiological studies, environmental studies, survey
research, economics, and so on. In this talk, I will discuss the issues induced from noisy data and some methods of handling such data.
Grace Y. Yi，女，博士，教授，博士生导师。2002年加拿大多伦多大学统计系获博士学位。现任加拿大滑铁卢大学统计精算系讲席教授。Grace Y. Yi教授长期从事统计的理论和应用的研究工作，并且担任国际知名刊物《The Canadian Journal of Statistics》的主编，其研究领域广泛，涉及生存分析, 高维数据分析，缺失数据，测量误差等。先后在国际统计学top期刊《The Journal of the Royal Statistical Society, SeriesB》，《Journal of the American Statistical Association》，《Biometrika》 以及一些著名统计期刊《Statistical Methods in Medical Research》，《Biometrics》，《Statistica Sinica》，《The Canadian Journal of Statistics》，《Biostatistics》，《Statistics in Medicines》等国际权威刊物上发表论文八十余篇。