Research Article
Multi-class Classification of Imbalanced Intelligent Data using Deep Neural Network
@ARTICLE{10.4108/airo.3486, author={Masoumeh Soleimani and Akram Sadat Mirshahzadeh}, title={Multi-class Classification of Imbalanced Intelligent Data using Deep Neural Network}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={2}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2023}, month={7}, keywords={Imbalanced Data, Dynamic Sampling, Deep Neural Network}, doi={10.4108/airo.3486} }
- Masoumeh Soleimani
Akram Sadat Mirshahzadeh
Year: 2023
Multi-class Classification of Imbalanced Intelligent Data using Deep Neural Network
AIRO
EAI
DOI: 10.4108/airo.3486
Abstract
In recent years, studies in the field of deep learning have made significant progress. These studies have focused more on datasets with balanced classification, and less research has been done on imbalanced datasets, which are of great importance in the real world and present significant challenges for classification. This article studies the problem of classifying imbalanced data, introduces dynamic sampling for deep neural networks, investigates the imbalanced multiclass problem, and proposes a dynamic sampling method for deep learning. In our proposed method, all samples are fed to the current deep neural network for each training iteration, and the accuracy, precision, and mean error of the deep neural network are estimated. The proposed method dynamically selects informative data for training the deep neural network. Comprehensive experiments were conducted to evaluate and understand its strengths and weaknesses. The results of 13 imbalanced multiclass datasets show that the proposed method outperforms other methods, such as initial sampling techniques, active learning, cost-sensitive learning, and reinforcement learning.
Copyright © 2023 Soleimani et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.