SGBBA: An Efficient Method for Prediction System in Machine Learning using Imbalance Dataset
Open accessAbstract
A real world big dataset with disproportionate classification is called imbalance dataset which badly impacts the predictive result of machine learning classification algorithms. Most of the datasets faces the class imbalance problem in machine learning. Most of the algorithms in machine learning work perfectly with about equal samples counts for every class. A variety of solutions have been suggested in the past time by the different researchers and applied to deal with the imbalance dataset. The performance of these methods is lower than the satisfactory level. It is very difficult to design an efficient method using machine learning algorithms without making the imbalance dataset to balance dataset. In this paper we have designed an method named SGBBA: an efficient method for prediction system in machine learning using Imbalance dataset. The method that is addressed in this paper increases the performance to the maximum in terms of accuracy and confusion matrix. The proposed method is consisted of two modules such as designing the method and method based prediction. The experiments with two benchmark datasets and one highly imbalanced credit card datasets are performed and the performances are compared with the performance of SMOTE resampling method. F-score, specificity, precision and recall are used as the evaluation matrices to test the performance of the proposed method in terms of any kind of imbalance dataset. According to the comparison of the result of the proposed method computationally attains the effective and robust performance than the existing methods.
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Federated Learning is an advanced machine learning method that requires large datasets.
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From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [233]
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Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
Self-citationStudy on IoT for SARS-CoV-2 with healthcare: present and future perspective
Rahman, A., Department of Computer Science and Engineering, N. I. O. T. E. A. R. (., Rahman, M., Kundu, D., Karim, M. R., Band, S. S., Sookhak, M., Department of Computer Science and Engineering, M. B. S. A. T. U., Department of Electrical and Electronic Engineering, N. I. O. T. E. A. R. (., Future Technology Research Center, C. O. F., et al. (2021) · Mathematical Biosciences and Engineering · doi:10.3934/mbe.2021475 · Cited by 22 · see citing works
Self-citation
- Authors
- Islam, S., Sara, U., Kawsar, A., Rahman, A., Kundu, D., Das, D., Rezaul, A. N. M., & Hasan, M.
- Affiliations
- Chittagong University of Engineering & Technology (BD)
- Venue
- International Journal of Advanced Computer Science and Applications
- Published
- 2021
- Volume
- 12
- Issue
- 3
- ISSN
- 2156-5570 (ISSN-L)
- 2158-107X
- OpenAlex
- W3140323468
- Cited by
- 37
- References
- 20
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 12 · 2023: 6 · 2022: 4 · 2021: 1
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Advanced Computer Science and Applications (publishedVersion, cc-by)
- International Journal of Advanced Computer Science and Applications (submittedVersion, cc-by)
- Funders
- Strong
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- class imbalance, predictive systems, classification accuracy, credit card fraud detection, SMOTE comparison, resampling
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