Twin Bounded Support Vector Machine with Capped Pinball Loss

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Huiru Wang, Xiaoqing Hong, Siyuan Zhang
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引用次数: 0

Abstract

In order to obtain a more robust and sparse classifier, in this paper, we propose a novel classifier termed as twin bounded support vector machine with capped pinball loss (CPin-TBSVM), which has the excellent properties of being insensitive to feature and label noise. Given that the proposed model is non-convex, we use the convex-concave procedure algorithm (CCCP) to solve a series of two smaller-sized quadratic programming problems to find the optimal solution. In the process of solving the iterative subproblem, the dual coordinate descent method (DCDM) is used for speeding up solving optimization problems. Moreover, we analyze its theoretical properties, including that the capped pinball loss satisfies Bayes’ rule and CPin-TBSVM has certain noise insensitivity and sparsity. The properties are verified on an artificial dataset as well. The numerical experiment is conducted on 24 UCI datasets and the results are compared with four other models which include SVM, TSVM, Pin-GTSVM and TPin-TSVM. The results show that the proposed CPin-TBSVM has a better classification effect and noise insensitivity.

Abstract Image

带有弹球损失上限的孪生有界支持向量机
为了获得更鲁棒且稀疏的分类器,我们在本文中提出了一种新型分类器,即带有弹球损失上限的孪生有界支持向量机(CPin-TBSVM),它具有对特征和标签噪声不敏感的优异特性。鉴于所提出的模型是非凸的,我们使用凸-凹过程算法(CCCP)来求解一系列两个较小的二次编程问题,以找到最优解。在求解迭代子问题的过程中,我们使用了双坐标下降法(DCDM)来加快优化问题的求解速度。此外,我们还分析了其理论特性,包括封顶弹球损失满足贝叶斯规则,CPin-TBSVM 具有一定的噪声不敏感性和稀疏性。这些特性也在一个人工数据集上得到了验证。在 24 个 UCI 数据集上进行了数值实验,并将实验结果与其他四种模型(包括 SVM、TSVM、Pin-GTSVM 和 TPin-TSVM)进行了比较。结果表明,所提出的 CPin-TBSVM 具有更好的分类效果和噪声不敏感性。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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