[1]何强,张娇阳.核对齐多核模糊支持向量机[J].智能系统学报,2019,14(6):1163-1169.[doi:10.11992/tis.201904050]
HE Qiang,ZHANG Jiaoyang.Kernel-target alignment multi-kernel fuzzy support vector machine[J].CAAI Transactions on Intelligent Systems,2019,14(6):1163-1169.[doi:10.11992/tis.201904050]
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HE Qiang,ZHANG Jiaoyang.Kernel-target alignment multi-kernel fuzzy support vector machine[J].CAAI Transactions on Intelligent Systems,2019,14(6):1163-1169.[doi:10.11992/tis.201904050]
核对齐多核模糊支持向量机
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第6期
页码:
1163-1169
栏目:
学术论文—机器学习
出版日期:
2019-11-05
- Title:
- Kernel-target alignment multi-kernel fuzzy support vector machine
- Keywords:
- kernels; support vector machines; rough set theory; supervised learning; fuzzy classification; fuzzy set membership functions; robustness; noise
- 分类号:
- TP181
- 摘要:
- 支持向量机（SVMs）是当前被广泛使用的机器学习技术，其通过最优分割超平面来提高分类器的泛化能力，在实际应用中表现优异。然而SVM也存在易受噪声影响，以及核函数选择等难题。针对以上问题，本文将基于核对齐的多核学习方法引入到模糊支持向量机（fuzzy support vector machine， FSVM）中，提出了模糊多核支持向量机模型（multiple kernel fuzzy support vector machine，MFSVM）。MFSVM通过模糊粗糙集方法计算每一样例隶属度；其次，利用核对齐的多核方法计算每一单核权重，并将组合核引入到模糊支持向量机中。该方法不仅提高了支持向量机的抗噪声能力，也有效避免了核选择难题。在UCI数据库上进行实验，结果表明本文所提方法具有较高的分类精度，验证了该方法的可行性与有效性。
- Abstract:
- Support vector machines (SVMs) are widely used machine learning techniques. They are used to construct an optimal hyper-plane and have an extraordinary generalization capability and good performance. However, SVMs are sensitive to noise, and it is difficult to select an appropriate kernel for SVMs. In this paper, we introduce kernel-target alignment-based multi-kernel learning method into fuzzy support vector machine (FSVM) and propose the kernel-target alignment-based multi-kernel fuzzy support vector machine (MFSVM). First, we assign the corresponding membership degree to each sample point by the fuzzy rough set method, and then calculate the kernel weight by the multi-kernel learning based on the kernel alignment. Then, the combined kernel is introduced into the fuzzy SVM. The proposed method not only improves the anti-noise ability of the SVM but also effectively avoids the problem of kernel selection. Experiments on nine datasets of the UCI database show that the proposed method has a high classification accuracy, which verifies its feasibility and effectiveness.
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备注/Memo
收稿日期:2019-04-20。
基金项目:国家自然科学基金项目（61473111）；北京建筑大学科学研究基金项目（KYJJ2017017）.
作者简介:何强,男,1977年生,副教授,主要研究方向为多核学习、监督学习、确定性信息处理。主持国家自然基金、河北省自然基金等多项。发表学术论文30余篇;张娇阳,女,1993年生,硕士,主要研究方向为多核学习、支持向量机。参与国家基金项目1项,发表学术论文3篇。
通讯作者:何强.E-mail:heqiang@bucea.edu.cn
更新日期/Last Update:
2019-12-25