Cost-Sensitive Support Vector Machine Using Randomized Dual Coordinate Descent Method for Big Class-Imbalanced Data Classification
Cost-sensitive support vector machine is one of the most popular tools to deal with class-imbalanced problem such as fault diagnosis. However, such data appear with a huge number of examples as well as features. Aiming at class-imbalanced problem on big data, a cost-sensitive support vector machine...
Saved in:
Main Authors: | Mingzhu Tang, Chunhua Yang, Kang Zhang, Qiyue Xie |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2014-01-01
|
Series: | Abstract and Applied Analysis |
Online Access: | http://dx.doi.org/10.1155/2014/416591 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
by: Yong Zhang, et al.
Published: (2013-01-01) -
Coordinate Descent-Based Sparse Nonnegative Matrix Factorization for Robust Cancer-Class Discovery and Microarray Data Analysis
by: Melisew Tefera Belachew
Published: (2021-01-01) -
Retracted: The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
by: Applied Bionics and Biomechanics
Published: (2023-01-01) -
Class Weighting Approach For Handling Imbalanced Data On Forest Fire Classification Using EfficientNet-B1
by: Arvinanto Bahtiar, et al.
Published: (2025-01-01) -
Adaptive Oversampling via Density Estimation for Online Imbalanced Classification
by: Daeun Lee, et al.
Published: (2025-01-01)