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Huang ZA, Sang Y, Sun Y, Lv J. Neural network with absent minority class samples and boundary shifting for imbalanced data classification. Neural Comput Appl 2023. [DOI: 10.1007/s00521-022-08135-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Sun L, Wang X, Ding W, Xu J. TSFNFR: Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Liu B, Xu M, Gao L, Yang J, Di X. A hybrid approach for high-dimensional optimization: Combining particle swarm optimization with mechanisms in neuro-endocrine-immune systems. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Yang M, Wang Z, Li Y, Zhou Y, Li D, Du W. Gravitation balanced multiple kernel learning for imbalanced classification. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07187-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Shaw SS, Ahmed S, Malakar S, Garcia-Hernandez L, Abraham A, Sarkar R. Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00314-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
AbstractMany real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.
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Pei W, Xue B, Shang L, Zhang M. Genetic programming for high-dimensional imbalanced classification with a new fitness function and program reuse mechanism. Soft comput 2020. [DOI: 10.1007/s00500-020-05056-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Multi-parameter online optimization algorithm of BP neural network algorithm in Internet of Things service. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04913-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Najib FM, Ismail RM, Badr NL, Gharib TF. Clustering based approach for incomplete data streams processing. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Fatma M. Najib
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Rasha M. Ismail
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Nagwa L. Badr
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Tarek F. Gharib
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
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