title: A Novel Classification Method for Syndrome Differentiation of Patients with AIDS.
abstract: We consider the analysis of an AIDS dataset where each patient is characterized by a list of symptoms and is labeled with one or more TCM syndromes. The task is to build a classifier that maps symptoms to TCM syndromes. We use the minimum reference set-based multiple instance learning (MRS-MIL) method. The method identifies a list of representative symptoms for each syndrome and builds a Gaussian mixture model based on them. The models for all syndromes are then used for classification via Bayes rule. By relying on a subset of key symptoms for classification, MRS-MIL can produce reliable and high quality classification rules even on datasets with small sample size. On the AIDS dataset, it achieves average precision and recall 0.7736 and 0.7111, respectively. Those are superior to results achieved by alternative methods.
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China ; Key Laboratory of Advanced Information Science and Network Technology of Beijing, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Liyun
authors.initials: L
authors.lastname: He
authors.affiliation: China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Xie
authors.affiliation: China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Guozheng
authors.initials: G
authors.lastname: Li
authors.affiliation: China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Baoyan
authors.initials: B
authors.lastname: Liu
authors.affiliation: China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Wang
authors.affiliation: China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Luo
authors.affiliation: China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Li
authors.affiliation: Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Xianghong
authors.initials: X
authors.lastname: Jing