Fuzzy Rough Support Vector Machine for Data Classification
Fuzzy Rough Support Vector Machine for Data Classification
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DOI：
10.4018/ijfsa.2016040103
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
A. Chaudhuri
中科院分区：
文献类型：
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作者：
A. Chaudhuri
本文的分类任务由FRSVM完成。它是FSVM和MFSVM的变体。模糊粗糙集兼顾了噪声样本的敏感性和不精确性的处理。将隶属度函数发展为特征空间中每个类的中心和半径的函数。它对决策面的采样起着重要的作用。训练样本可以是线性的，也可以是非线性的。在非线性训练样本中，将输入空间映射到高维特征空间来计算分离面。不同的输入点对决策面有不同的贡献。分类器的性能是根据支持向量的数量来评估的。针对c值考察了变异对FRSVM预测和泛化的影响。它有效地解决了不平衡和重叠的类问题，对未见数据进行了归一化，并放松了特征与标签之间的依赖关系。在合成数据集和真实数据集上的实验结果都表明，FRSVM在降低异常值影响方面的性能优于现有svm。
In this paper, classification task is performed by FRSVM. It is variant of FSVM and MFSVM. Fuzzy rough set takes care of sensitiveness of noisy samples and handles impreciseness. The membership function is developed as function of cener and radius of each class in feature space. It plays an important role towards sampling the decision surface. The training samples are either linear or nonlinear separable. In nonlinear training samples, input space is mapped into high dimensional feature space to compute separating surface. The different input points make unique contributions to decision surface. The performance of the classifier is assessed in terms of the number of support vectors. The effect of variability in prediction and generalization of FRSVM is examined with respect to values of C. It effectively resolves imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. Experimental results on both synthetic and real datasets support that FRSVM achieves superior performance in reducing outliers' effects than existing SVMs.