A joint sparse clustering and classification approach with applications to hospitalization prediction

被引:0
作者
Xu, Tingting [1 ]
Brisimi, Theodora S.
Wang, Taiyao
Dai, Wuyang
Paschalidis, Ioannis Ch.
机构
[1] Boston Univ, Dept Elect & Comp Engn, Div Syst Engn, Boston, MA 02215 USA
来源
2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC) | 2016年
关键词
Clustering; classification; large-scale problems; healthcare; predictive analytics; heart diseases;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We develop a new method for a particular type of a classification problem, where the positive class is a mixture of multiple clusters and the negative class is drawn from a single cluster. The new method employs an alternating optimization approach, which jointly discovers the clusters in the positive class, and at the same time, optimizes the classifiers that separate each positive cluster from the negative samples. The classifiers are designed under the Support Vector Machines (SVM) framework with double regularizations, and the whole alternating process is shown to converge. We compare this new method to the conventional SVM with a linear kernel or an RBF kernel and two other hierarchical classifiers which first cluster once and then classify. Experimental results with both simulated data and actual data demonstrate better prediction accuracy, as well as, successful cluster detection.
引用
收藏
页码:4566 / 4571
页数:6
相关论文
共 7 条
[1]  
[Anonymous], 2001, SPRINGER SERIES STAT, DOI [DOI 10.1007/978-0-387-21606-5, 10.1007/978-0-387-21606-5]
[2]  
Breiman F, 1984, OLSHEN STONE CLASSIF
[3]  
Dai W., 2015, 2 WORKSH DAT MIN MED
[4]   Mixing Linear SVMs for Nonlinear Classification [J].
Fu, Zhouyu ;
Robles-Kelly, Antonio ;
Zhou, Jun .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (12) :1963-1975
[5]  
Gu Q., 2013, P 16 INT C ART INT S, P307
[6]  
Pele O., 2013, Proceedings of The 30th International Conference on Machine Learning, P205
[7]  
Sontag E. D., 1998, Neural Networks and Machine Learning. Proceedings, P69