A1262
Title: Ensemble LDA via the modified Cholesky decomposition
Authors: Zhenguo Gao - Shanghai Jiao Tong University (China) [presenting]
Xinye Wang - Shanghai Jiao Tong University (China)
Xiaoning Kang - Dongbei University of Finance and Economics (China)
Abstract: A binary classification problem in the high-dimensional settings is studied via ensemble learning with each base classifier constructed from the linear discriminant analysis (LDA), and these base classifiers are integrated by the weighted voting. The precision matrix in the LDA rule is estimated by the modified Cholesky decomposition (MCD), which is able to provide a set of precision estimates by considering multiple variable orderings and, hence, yield a group of different LDA classifiers. Such available LDA classifiers are then integrated to improve the classification performance. The simulation and the application studies are conducted to demonstrate the merits of the proposed method.
