支持向量机
加权
核(代数)
人工智能
离群值
铰链损耗
模式识别(心理学)
水准点(测量)
计算机科学
二元分类
模糊逻辑
操作员(生物学)
排序支持向量机
数学
二进制数
机器学习
化学
地理
抑制因子
放射科
组合数学
基因
算术
转录因子
医学
生物化学
大地测量学
作者
Sebastián Maldonado,José M. Merigó,J. Jaime Miranda
出处
期刊:IEEE Transactions on Fuzzy Systems
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:28 (9): 2143-2150
被引量:25
标识
DOI:10.1109/tfuzz.2019.2930942
摘要
A weighting strategy for handling outliers in binary classification using support vector machine (SVM) is proposed in this article. The traditional SVM model is modified by introducing an induced ordered weighted averaging (IOWA) operator, in which the hinge loss function becomes an ordered weighted sum of the SVM slack variables. These weights are defined using IOWA quantifiers, while the order is induced via fuzzy density-based methods for outlier detection. The proposal is developed for both linear and kernel-based classification using the duality theory and the kernel trick. Our experimental results on well known benchmark datasets demonstrate the virtues of the proposed IOWA-SVM, which achieved the best average performance compared to other machine learning approaches of similar complexity.
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