- Open Access
- Article
An Integrated Approach to Manage Imbalanced Datasets using PCA with Neural Networks
by Swarup Kumar Mondal 
and Anindya Sen
1 Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, 700107, India
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 1-12, 2024; DOI: 10.55708/js0310001
Keywords: Imbalanced data, Regression, Deep Neural Network, Artificial Neural Network, Support Vector Machine
Received: 19 August 2024, Revised: 20 September 2024, Accepted: 21 September 2024, Published Online: 11 October 2024
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Lois, L. J., Liu, Q., Quan, J., Ye, Q., Zhang, X., Hua, Y., & Li, X. (2024). Application of deuterium oxide (D2O) isotope tracing technique for land grid array package failure analysis. Journal of Engineering Research and Sciences, 3(10), 1-20. https://doi.org/10.55708/js0310001
Chicago/Turabian Style
Lois, Liao Jinzhi, Liu Qing, Quan Jing, Ye Qing, Zhang Xi, Hua Younan, and Li Xiaomin. “Application of Deuterium Oxide (D2O) Isotope Tracing Technique for Land Grid Array Package Failure Analysis.” Journal of Engineering Research and Sciences 3, no. 10 (2024): 1-20. https://doi.org/10.55708/js0310001.
IEEE Style
L. J. Lois, Q. Liu, J. Quan, Q. Ye, X. Zhang, Y. Hua, and X. Li, “Application of deuterium oxide (D2O) isotope tracing technique for land grid array package failure analysis,” Journal of Engineering Research and Sciences, vol. 3, no. 10, pp. 1-20, 2024, doi: 10.55708/js0310001.
Imbalanced dataset handling in real time is one of the most challenging tasks in predictive modelling. This work handles the critical issues arising in imbalanced dataset with implementation of artificial neural network and deep neural network architecture. The usual machine learning algorithms fails to achieve desired throughput with certain input circumstances due to mismatched class ratios in the sample dataset. Dealing with imbalanced dataset leads to performance degradation and interpretability issue in traditional ML architectures. For regression tasks, where the target variable is continuous, the skewed data distribution is major issue. In this study, we have investigated a detailed comparison of traditional ML algorithms and neural networks with dimensionality reduction method to overcome this problem. Principle component analysis has been used for feature selection and analysis on real time satellite-based air pollution dataset. Five regression algorithms Multilinear, Ridge, Lasso, Elastic Net and SVM regression is combined with PCA and non PCA to interpret the outcome. To address unbalanced datasets in real-time, deep neural networks and artificial neural network architectures have been developed. Each model’s experiments and mathematical modelling is done independently. The Deep neural network is superior compared to other conventional models for performance measures of target variable in imbalanced datasets.
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