Perceptron Ranking Using Interval Labels with Ramp Loss for Online Ordinal Regression

被引：0

作者：

Zhang, Cuiqing ^{[1
]}

Zhang, Maojun ^{[1
,2
]}

Liang, Xijun ^{[3
]}

Xia, Zhonghang ^{[4
]}

Nan, Jiangxia ^{[1
,2
]}

机构：

[1] Guilin Univ Elect Technol, Sch Math & Comp Sci, Guilin 541004, Peoples R China

[2] Suzhou Univ Sci & Technol, Sch Business, Suzhou 215009, Peoples R China

[3] China Univ Petr, Coll Sci, Qingdao 266580, Peoples R China

[4] Western Kentucky Univ, Sch Engn & Appl Sci, Bowling Green, KY 42101 USA

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2020年 / 2020卷 / 2020期

基金：

中国国家自然科学基金;

关键词：

MODELS;

D O I：

10.1155/2020/8866257

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Due to its wide applications and learning efficiency, online ordinal regression using perceptron algorithms with interval labels (PRIL) has been increasingly applied to solve ordinal ranking problems. However, it is still a challenge for the PRIL method to handle noise labels, in which case the ranking results may change dramatically. To tackle this problem, in this paper, we propose noise-resilient online learning algorithms using ramp loss function, called PRIL-RAMP, and its nonlinear variant K-PRIL-RAMP, to improve the performance of PRIL method for noisy data streams. The proposed algorithms iteratively optimize the decision function under the framework of online gradient descent (OGD), and we justify the algorithms by showing the order preservation of thresholds. It is validated in the experiments that both approaches are more robust and efficient to noise labels than state-of-the-art online ordinal regression algorithms on real-world datasets.

引用

页数：15