Abstract
Imbalanced data are a common classification problem. Since it occurs in most real fields, this trend is increasingly important. It is of particular concern for highly imbalanced datasets (when the class ratio is high). Different techniques have been developed to deal with supervised learning sets. SMOTE is a well-known method for over-sampling that discusses imbalances at the level of the data. In the area, unequal data are widely distributed, and ensemble learning algorithms are a more efficient classifier in classifying imbalances. SMOTE synthetically contrasts two closely connected vectors. The learning algorithm itself, however, is not designed for imbalanced results. The simple ensemble idea, as well as the SMOTE algorithm, works with imbalanced data. There are detailed studies about imbalanced data problems and resolving this problem through several approaches. There are various approaches to overcome this problem, but we mainly focused on SMOTE and extreme learning machine algorithms.
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References
Shen F, Zhao X, Li Z, Li K, Meng Z (2019) A novel ensemble classification model based on neural networks and a classifier optimization technique for imbalanced credit risk evaluation. Phys A Statist Mech Its Appl 526:1–17. https://doi.org/10.1016/j.physa.2019.121073
Rustogi R, Prasad A (2019) Swift imbalance data classification using SMOTE and extreme learning machine. In: Second international conference on computational intelligence in data science (ICCIDS), vol 978, pp 1–8
Shelke MS (2017) A review on imbalanced data handling using undersampling and oversampling technique. Int J Recent Trends Eng Res (IJRTER) 03(04):1–7
Gupta C, Gill NS (2020) ML techniques and extreme learning machine for early breast cancer prediction. Int J Innov Technol Explor Eng (IJITEE) 9(4). ISSN: 2278-3075
Birla S, Kohli K, Dutta A (2016) Machine learning on imbalanced data in credit risk. In: 2016 IEEE 7th annual information technology, electronics and mobile communication conference (IEMCON), vol 978, pp 1–6.https://doi.org/10.1109/iemcon.2016.7746326
Ochani M, Sawarkar SD, Narwane S (2019) A novel approach to handle class imbalance: a survey. Int J Eng Dev Res (IJEDR) 7(2): 1–9. ISSN: 2321-9939
Elreedy D, Atiya AF (2019) A Comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci 502:32–63. https://doi.org/10.1016/j.ins.2019.07.070
Deng B, Zhang X, Gong W, Shang D (2019) An overview of extreme learning machine. In: 2019 4th international conference on control, robotics and cybernetics (CRC), vol. 978, pp. 1–7. https://doi.org/10.1016/j.ins.2019.07.070
Abbas M, Albadra A, Tiuna S (2017) Extreme learning machine: a review. Int J Appl Eng Res 12(14):4610–4623. ISSN 0973-4562
Kajaree D (2007) A survey on machine learning: concept, algos and applications. Int J Innov Res Comput Commun Eng 5(2):1–12. (An ISO 3297: Certified Organization), Website: www.ijircce.com
Yuan Z, Zhao P (2019) An improved ensemble learning for imbalanced data classification. In: 2019 IEEE 8th joint international information technology and artificial intelligence conference (ITAIC), vol 978, pp 1–4. https://doi.org/10.1109/itaic.2019.8785887
Gameng HA, Gerardo BB, Medina RP (2019) Modified adaptive synthetic SMOTE to improve classification performance in imbalanced datasets. In: IEEE 6th international conference on engineering technologies and applied sciences (ICETAS), Kuala Lumpur, Malaysia, vol 978, pp 1–5. https://doi.org/10.1109/ICETAS48360.2019.9117287
Khadijah SNE, Kusumaningrum R (2018) The study of synthetic minority over-sampling technique (SMOTE) and weighted extreme learning machine for handling imbalance problem on multiclass microarray classification. In: International conference on informatics and computational sciences (ICICoS), Semarang, Indonesia, pp 1–6. https://doi.org/10.1109/ICICOS.2018.8621657
Mohasseb M, Cocea M, Liu H (2018) improving imbalanced question classification using structured smote based approach. In: International conference on machine learning and cybernetics (ICMLC), Chengdu, pp 593–597. https://doi.org/10.1109/ICMLC.2018.8527028
Sanguanmak Y, Hanskunatai A (2016) DBSM: the combination of DBSCAN and SMOTE for imbalanced data classification. In: international joint conference on computer science and software engineering (JCSSE), KhonKaen, pp 1–5. https://doi.org/10.1109/JCSSE.2016.7748928
Matsuda K, Murase K (2016) Single-layered complex-valued neural network with SMOTE for imbalanced data classification. In: 2016 joint 8th international conference on soft computing and intelligent systems (SCIS) and 17th international symposium on advanced intelligent systems (ISIS). https://doi.org/10.1109/scis-isis.2016.0079
Li K, Zhang W, Lu Q, Fang X (2014) An improved SMOTE, imbalanced data classification method, based on support degree. In: 2014 international conference on identification, information, and knowledge in the internet of things. https://doi.org/10.1109/iiki.2014.14
Liu S, Zhu P, Qin S (2018) An improved weighted KNN algorithm for imbalanced data classification. In: 2018 IEEE 4th international conference on computer and communications (ICCC). https://doi.org/10.1109/compcomm.2018.8780580
Zhou T, Liu W, Zhou C, Chen L (2018) GAN-based semi-supervised for imbalanced data classification. In: 2018 4th international conference on information management (ICIM). https://doi.org/10.1109/infoman.2018.8392662
Shimizu R, Asako K, Ojima H, Morinaga S, Hamada M, Kuroda T (2018) Balanced mini-batch training for imbalanced image data classification with neural network. In: 2018 first international conference on artificial intelligence for industries (AI4I). https://doi.org/10.1109/ai4i.2018.8665709
Li K, **e P, Zhai J, Liu W (2017) An improved Adaboost algorithm for imbalanced data based on weighted KNN. In: 2017 IEEE 2nd international conference on big data analysis (ICBDA). https://doi.org/10.1109/icbda.2017.8078849
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Goyal, A., Rathore, L., Kumar, S. (2021). A Survey on Solution of Imbalanced Data Classification Problem Using SMOTE and Extreme Learning Machine. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_4
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