Multi-instance classification based on regularized multiple criteria linear programming

被引：1
作者：
Zhiquan Qi
Yingjie Tian
Yong Shi
机构：
[1] Chinese Academy of Sciences,Research Center on Fictitious Economy and Data Science
[2] University of Nebraska at Omaha,College of Information Science and Technology
来源：
Neural Computing and Applications | 2013年 / 23卷
关键词：
Data mining; Multi-instance learning; Regularized multiple criteria linear programming; Support vector machine;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Regularized multiple criteria linear programming (RMCLP) model is a new powerful method for classification and has been used in various real-life data mining problems. In this paper, a new Multi-instance Classification method based on RMCLP was proposed (called MI-RMCLP), which includes two algorithms for linearly separable case and nonlinearly case separately. The key point of this method, instead of a mixed integer quadratic programming in MI-SVM, is that it is able to deal with multi-instance learning problem by an iterative strategy solving sequential quadratic programming problems. All experiment results have shown that MI-RMCLP method can converge to the optimal value in limited iterative steps and be a competitive method in multi-instance learning classification.
引用
收藏
页码：857 / 863
页数：6
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