Enhanced automatic twin support vector machine for imbalanced data classification
作者：
Highlights：
• We propose a TWSVM-based method that includes a kernel enhancement within the TWSVM formulation for imbalanced data classification.
• Our proposal, termed enhanced automatic TWSVM’ (EATWSVM), allows representing the input samples in a high-dimensional space of possible infinity dimension during the classifier training.
• We rule a Gaussian-based similarity through a Mahalanobis distance to learn the EATWSVM’s kernel function using a centered kernel alignment (CKA) -based method.
• We suggest a suitable range to fix the regularization parameters concerning both the dataset imbalanced ratio and overlap.
• EATWSVM obtains a remarkable trade-off between classification performance and training time, without the need of a sophisticated prior user knowledge concerning the algorithm tuning.
摘要
•We propose a TWSVM-based method that includes a kernel enhancement within the TWSVM formulation for imbalanced data classification.•Our proposal, termed enhanced automatic TWSVM’ (EATWSVM), allows representing the input samples in a high-dimensional space of possible infinity dimension during the classifier training.•We rule a Gaussian-based similarity through a Mahalanobis distance to learn the EATWSVM’s kernel function using a centered kernel alignment (CKA) -based method.•We suggest a suitable range to fix the regularization parameters concerning both the dataset imbalanced ratio and overlap.•EATWSVM obtains a remarkable trade-off between classification performance and training time, without the need of a sophisticated prior user knowledge concerning the algorithm tuning.
论文关键词：Imbalanced data,Kernel methods,Twin support vector machines
论文评审过程：Received 21 November 2019, Revised 8 April 2020, Accepted 7 May 2020, Available online 3 June 2020, Version of Record 11 June 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107442
