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Sparse elastic net multi-label rank support vector machine with pinball loss and its applications
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-02-24 , DOI: 10.1016/j.asoc.2021.107232
Hongmei Wang , Yitian Xu

Multi-label rank support vector machine (RankSVM) is an effective technique to deal with multi-label classification problems, which has been widely used in various fields. However, it is sensitive to noise points and cannot delete redundant features for high dimensional problems. Therefore, to address the above two limitations, a sparse elastic net multi-label RankSVM with pinball loss (pin-ENR) is first proposed in this paper. On the one hand, pinball loss is employed to enhance the robustness. On the other hand, it adopts the sparse elastic net regularization, so that it can do variable selection. However, it still has challenges for large-scale problems with a huge number of features, samples, and labels. Therefore, motivated by the sparsity of pin-ENR, a safe simultaneous feature and label-pair elimination rule is further constructed for accelerating pin-ENR, which is termed as FLER-pin-ENR. Its main idea is to delete a large number of inactive features and label-pairs simultaneously before training without sacrificing accuracy. Numerical experiments on four synthetic and seven benchmark datasets demonstrate the feasibility and validity. Moreover, we apply our FLER-pin-ENR to the diagnosis of diabetes complications and the natural scene image classification problems, which further verifies the practicability of our proposed method.



中文翻译:

弹球损失的稀疏弹性网多标签秩支持向量机及其应用

多标签等级支持向量机(RankSVM)是一种有效的解决多标签分类问题的技术,已广泛应用于各个领域。但是,它对噪声点很敏感,并且不能删除高维问题的冗余特征。因此,为解决上述两个局限性,本文首先提出了一种具有弹球损失的稀疏弹性网状多标签RankSVM(pin-ENR)。一方面,采用弹球损失来增强鲁棒性。另一方面,它采用稀疏的弹性网正则化,因此可以进行变量选择。但是,对于具有大量功能,示例和标签的大规模问题,它仍然具有挑战性。因此,受pin-ENR稀疏的影响,进一步构造了安全的同时特征和标签对消除规则来加速pin-ENR,这被称为FLER-pin-ENR。其主要思想是在训练之前同时删除大量不活动的功能和标签对,而又不牺牲准确性。在四个综合数据集和七个基准数据集上的数值实验证明了可行性和有效性。此外,我们将我们的FLER-pin-ENR用于糖尿病并发症的诊断和自然场景图像分类问题,从而进一步验证了我们提出的方法的实用性。在四个综合数据集和七个基准数据集上的数值实验证明了可行性和有效性。此外,我们将我们的FLER-pin-ENR用于糖尿病并发症的诊断和自然场景图像分类问题,从而进一步验证了我们提出的方法的实用性。在四个综合数据集和七个基准数据集上的数值实验证明了可行性和有效性。此外,我们将我们的FLER-pin-ENR用于糖尿病并发症的诊断和自然场景图像分类问题,从而进一步验证了我们提出的方法的实用性。

更新日期:2021-03-03
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