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L-SVM: A radius-margin-based SVM algorithm with LogDet regularization

机译：L-SVM：具有LogDet正则化的基于半径余量的SVM算法

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摘要

Theoretically, support vector machines (SVMs) have general error bounds along a radius-margin ratio, while conventional SVMs consider only the maximization of the margin and ignore the minimization of the radius, which is sensitive to affine data transformations. Thus, conventional SVMs can be improved by controlling both the radius and the margin. Several SVM variants based on radius-margin ratio error bounds have been proposed to integrate the radius and margin. However, most of these either require a diagonal transformation matrix or are computationally expensive to optimize. In this paper, we propose a novel radius-margin-based SVM model with LogDet regularization called L-SVM. In our model, we consider the radius and introduce a negative LogDet term to improve the model accuracy. We also adopt a two-step alternating minimization strategy to obtain an optimal solution, which leads to impressive computational improvements. Our experimental results validate the performance of the L-SVM and show that the L-SVM achieves significantly higher accuracy and efficiency compared to conventional SVMs and some other state-of-the-art radius-margin-based SVM methods. In addition, we apply our proposed L-SVM to solve transaction fraud problems and propose a framework for an L-SVM-based fraud detection system. (C) 2018 Elsevier Ltd. All rights reserved.
机译：从理论上讲，支持向量机（SVM）具有沿半径-边距比率的通用误差范围，而常规SVM仅考虑边距的最大化而忽略半径的最小化，这对仿射数据转换很敏感。因此，可以通过控制半径和边距两者来改善传统的SVM。已经提出了几种基于半径-边缘比率误差范围的SVM变体来集成半径和边缘。但是，其中大多数都需要对角线变换矩阵，或者优化起来计算量很大。在本文中，我们提出了一种具有LogDet正则化的新型基于半径边距的SVM模型，称为L-SVM。在我们的模型中，我们考虑半径并引入负LogDet项以提高模型的准确性。我们还采用了两步交替最小化策略来获得最佳解决方案，从而带来了令人印象深刻的计算改进。我们的实验结果验证了L-SVM的性能，并表明L-SVM与常规SVM和其他一些基于半径边距的SVM方法相比，具有更高的准确性和效率。此外，我们将我们提出的L-SVM应用于解决交易欺诈问题，并提出了基于L-SVM的欺诈检测系统的框架。 （C）2018 Elsevier Ltd.保留所有权利。

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  • 来源
    《Expert Systems with Application》 |2018年第7期|113-125|共13页
  • 作者

    Du Jia-Zhi; Lu Wei-Gang; Wu Xiao-He; Dong Jun-Yu; Zuo Wang-Meng;

  • 作者单位

    Ocean Univ China, Dept Educ Technol, 238 Songling Rd, Qingdao 266100, Shandong, Peoples R China;

    Ocean Univ China, Dept Educ Technol, 238 Songling Rd, Qingdao 266100, Shandong, Peoples R China;

    Harbin Inst Technol, Sch Comp Sci & Technol, 92 Xidazhi St, Harbin 150006, Heilongjiang, Peoples R China;

    Ocean Univ China, Dept Comp Sci & Technol, Room 110,Teaching Ctr Fundamental Courses Bldg, Qingdao 266100, Shandong, Peoples R China;

    Harbin Inst Technol, Sch Comp Sci & Technol, 92 Xidazhi St, Harbin 150006, Heilongjiang, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Support vector machine; Radius-margin ratio; Error bounds; LogDet regularization; Fraud detection system;

    机译：支持向量机;半径余量比;误差范围;LogDet正则化;欺诈检测系统;

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