title: Dealing with heterogeneous classification problem in the framework of multi-instance learning.
abstract: To deal with heterogeneous classification problem efficiently, each heterogeneous object was represented by a set of measurements obtained on different part of it, and the heterogeneous classification problem was reformulated in the framework of multi-instance learning (MIL). Based on a variant of count-based MIL assumption, a maximum count least squares support vector machine (maxc-LS-SVM) learning algorithm was developed. The algorithm was tested on a set of toy datasets. It was found that maxc-LS-SVM inherits all the sound characters of both LS-SVM and MIL framework. A comparison study between the proposed approach and the other two MIL approaches (i.e., mi-SVM and MI-SVM) was performed on a real wolfberry fruit spectral dataset. The results demonstrate that by formulating the heterogeneous classification problem as a MIL one, the heterogeneous classification problem can be solved by the proposed maxc-LS-SVM algorithm effectively.
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Zhaozhou
authors.initials: Z
authors.lastname: Lin
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Shuaiyun
authors.initials: S
authors.lastname: Jia
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Gan
authors.initials: G
authors.lastname: Luo
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Xingxing
authors.initials: X
authors.lastname: Dai
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Xu
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China.
authors.forename: Zhisheng
authors.initials: Z
authors.lastname: Wu
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China; Research Center of TCM-information Engineering, State Administration of Traditional Chinese Medicine of the People׳s Republic of China, Beijing 100102, China. Electronic address: xyshi@126.com.
authors.forename: Xinyuan
authors.initials: X
authors.lastname: Shi
authors.affiliation: College of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100102, China; Research Center of TCM-information Engineering, State Administration of Traditional Chinese Medicine of the People׳s Republic of China, Beijing 100102, China. Electronic address: yjqiao@263.net.
authors.forename: Yanjiang
authors.initials: Y
authors.lastname: Qiao
mesh_terms: D005638:Fruit / Q000737:chemistry; D016018:Least-Squares Analysis; D032305:Lycium / Q000737:chemistry; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*; D019265:Spectroscopy, Near-Infrared / Q000706:statistics & numerical data*; D060388:Support Vector Machine
keywords: Error-Correcting Output Codes (ECOC); Geographical origins; Heterogeneous spectra; Maximum count least square support vector machine (maxc-LS-SVM); Multi-instance learning (MIL)