Analysis of k-partite ranking algorithm in area under the receiver operating characteristic curve criterion

被引：38

作者：

Gao, Wei ^{[1
]}

Wang, Weifan ^{[2
]}

机构：

[1] Yunnan Normal Univ, Sch Informat & Technol, Kunming 650500, Yunnan, Peoples R China

[2] Zhejiang Normal Univ, Dept Math, Jinhua, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS | 2018年 / 95卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Statistical learning theory; k-partite ranking; AUC criterion; shatter coefficient; margin; MARGIN-BASED RANKING; GENERALIZATION BOUNDS; LEARNING-THEORY; ONTOLOGY; CLASSIFICATION; STABILITY;

D O I：

10.1080/00207160.2017.1322688

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The k-partite ranking, as an extension of bipartite ranking, is widely used in information retrieval and other computer applications. Such implement aims to obtain an optimal ranking function which assigns a score to each instance. The AUC (Area Under the ROC Curve) measure is a criterion which can be used to judge the superiority of the given k-partite ranking function. In this paper, we study the k-partite ranking algorithm in AUC criterion from a theoretical perspective. The generalization bounds for the k-partite ranking algorithm are presented, and the deviation bounds for a ranking function chosen from a finite function class are also considered. The uniform convergence bound is expressed in terms of a new set of combinatorial parameters which we define specially for the k-partite ranking setting. Finally, the generally margin-based bound for k-partite ranking algorithm is derived.

引用

页码：1527 / 1547

页数：21

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