IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data
Imbalanced datasets pose significant challenges in the field of machine learning, as they consist of samples where one class (majority) dominates over the other class (minority). Although AdaBoost is a popular ensemble method known for its good performance in addressing various problems, it fails wh...
Saved in:
Main Authors: | SeyedEhsan Roshan, Jafar Tanha, Farzad Hallaji, Mohammad-reza Ghanbari |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2023-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2023/2176891 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Class Weighting Approach For Handling Imbalanced Data On Forest Fire Classification Using EfficientNet-B1
by: Arvinanto Bahtiar, et al.
Published: (2025-01-01) -
About the confusion-matrix-based assessment of the results of imbalanced data classification
by: V. V. Starovoitov, et al.
Published: (2021-03-01) -
Imbalanced Data Sets Classification Based on SVM for Sand-Dust Storm Warning
by: Yonghua Xie, et al.
Published: (2015-01-01) -
A Semi-Supervised Learning Approach to Quality-Based Web Service Classification
by: Mehdi Nozad Bonab, et al.
Published: (2024-01-01) -
Machine Learning Classifiers and Data Synthesis Techniques to Tackle with Highly Imbalanced COVID-19 Data
by: Avaz Naghipour, et al.
Published: (2024-12-01)