Ordinal Hyperplane Loss

Bob Vanderheyden, Ying Xie
2018 2018 IEEE International Conference on Big Data (Big Data)  
This research presents the development of a new framework for analyzing ordered class data, commonly called "ordinal class" data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop
more » ... e classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize the ordering information of classes. Alternatively, the researcher may choose to treat the ordered classes as though they are continuous values. This strategy imposes a strong xi From the first day, all of you treated me like any other student and were a terrific help in getting through several of our challenging courses. The opportunity to work and study with you helped me survive the program. I also want to extend a special thank you to Bogdan. You seemed to always draw the short straw, sticking you with me as a project partner. Thank you for helping me survive those projects. Speaking of project partners, thank you Jessica Rudd, for being my project partner when Bogdan was not in the class that I was taking. Our conversations outside of course work were special for me. You will always be a dear friend. I also want to thank Yiyun Zhou and Dr. Linh Le for all of the help that you provided. From helping me understand some nuances of Tensorflow and Theano, to helping me work through challenges that I encountered in trying to develop deep learning models. You both have always been willing and capable collaborators and sounding boards. Thank you to the rest of the students, in other cohorts. This program will quickly be recognized as world class, when you complete your work. Our cohort is the first to finish, but yours may be the best to finish.
doi:10.1109/bigdata.2018.8622079 dblp:conf/bigdataconf/VanderheydenX18 fatcat:yj4ttuibt5ayvefjv6wsmfhb54