全球智库信息集成服务系统(G2TT): An Efficient Mixed Integer Programming Algorithm for Minimizing the Training Sample Misclassification Cost in Two-group Classification.

G2TT

来源类型	Monograph (IIASA Working Paper)
规范类型	论文
	An Efficient Mixed Integer Programming Algorithm for Minimizing the Training Sample Misclassification Cost in Two-group Classification.
	Duarte Silva AP; Stam A
发表日期	1996
出版者	IIASA, Laxenburg, Austria: WP-96-093
出版年	1996
语种	英语
摘要	In this paper, we introduce the Divide and Conquer (D&C) algorithm, a computationally efficient algorithm for determining classification rules which minimize the training sample misclassification cost in two-group classification. This classification rule can be derived using mixed integer programming (MIP) techniques. However, it is well-documented that the complexity of MIP-based classification problems grows exponentially as a function of the size of the training sample and the number of attributes describing the observations, requiring special-purpose algorithms to solve even small size problems within a reasonable computational time. The D&C algorithm derives its name from the fact that it relies, a.o., on partitioning the problem in smaller, more easily handled subproblems, rendering it substantially faster than previously proposed algorithms.
主题	Institute Scholars (INS)
URL	http://pure.iiasa.ac.at/id/eprint/4934/
来源智库	International Institute for Applied Systems Analysis (Austria)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/124566
推荐引用方式 GB/T 7714	Duarte Silva AP,Stam A. An Efficient Mixed Integer Programming Algorithm for Minimizing the Training Sample Misclassification Cost in Two-group Classification.. 1996.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
WP-96-093.pdf（993KB）	智库出版物		限制开放	CC BY-NC-SA	浏览