Flexible Margin-Based Classification Techniques
Seo Young Park
Department of Statistics and Operations Research
University of North Carolina at Chapel Hill
Classification is a very useful statistical tool for information extraction. Among numerous classification methods, margin-based classification techniques have attracted a lot of attention. In this talk, I will present several new margin-based classifiers, via modifying loss functions of two well-known classifiers, Penalized Logistic Regression (PLR) and the Support Vector Machine (SVM). For binary classification, we propose three new classification techniques, Robust Penalized Logistic Regression (RPLR), Bounded Contraint Machine (BCM), and the Balancing Support Vector Machine (BSVM). For multicategory classification, we propose the efficient multicategory Combined Least Squares (CLS) classifier. We study properties of the new methods and provide efficient computational algorithms. Simulated and microarray gene expression data analysis examples are used to demonstrate competitive performance of the proposed methods.
16 March, 2010 E1 TBA 4:40 pm