Expediting the accuracy-improving process of SVMs for class imbalance learning

Expediting the accuracy-improving process of SVMs for class imbalance learning

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associa...

Full description

Saved in:
Bibliographic Details
Main Authors: CAO, Bin, LIU, Yuqi, HOU, Chenyu, FAN, Jing, ZHENG, Baihua, JIN, Jianwei
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Support vector machines
Tuning
Optimization
Kernel
Training
Standards
Fans
Databases and Information Systems
Theory and Algorithms
Online Access:https://ink.library.smu.edu.sg/sis_research/5097
https://ink.library.smu.edu.sg/context/sis_research/article/6100/viewcontent/15._Expediting_the_Accuracy_improving_Process_of_XVMS_of_Class_Imbalance_Learning_TKDEFeb2020.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary:To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose ATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. ATEC distinguishes itself from all existing parameter tuning strategies by two main features: (1) it can evaluate how effective an error cost is in terms of classification accuracy; and (2) it changes the error cost in the right direction if it is not effective. Extensive experiments show that compared with the state-of-art methods, SVMs that are equipped with ATEC can not only obtain comparable improvements in terms of F1 score of minority class, area under the precision-recall curve (AUC-PR) and area under the ROC curve (AUC-ROC) scores, but also outperform the grid-search parameter tuning strategy by two orders of magnitude in terms of the training time when a high F1 score is required.

Similar Items