Evaluation and Enhancement of Standard Classifier Performance by Resolving Class Imbalance Issue Using Smote-Variants Over Multiple Medical Datasets (original) (raw)

• Japkowicz N, Stephen S. The class imbalance problem: a systematic study. Intell Data Anal. 2002;6(5):429–49.
  Article MATH Google Scholar
• Chawla NV, Japkowicz N, Kotcz A. Special issue on learning from imbalanced data sets. ACM SIGKDD Explor Newsl. 2004;6(1):1–6.
  Article MATH Google Scholar
• Wei W, Li J, Cao L, Ou Y, Chen J. Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web. 2013;16(4):449–75.
  Article MATH Google Scholar
• Glauner P, Boechat A, Dolberg L, State R, Bettinger F, Rangoni Y, Duarte D. Large-scale detection of non-technical losses in imbalanced data sets. In: 2016 IEEE power and energy society innovative smart grid technologies conference (ISGT). IEEE; Minneapolis, MN, USA, 2016. p. 1–5.
• Kumar V. Evaluation of computationally intelligent techniques for breast cancer diagnosis. Neural Comput Appl. 2021;33(8):3195–208.
  Article MATH Google Scholar
• Lei W, Zhang R, Yang Y, Wang R, Zheng WS. Class-center involved triplet loss for skin disease classification on imbalanced data. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE; Iowa City, IA, USA, 2020. p. 1–5.
• Tong X, Feng Y, Li JJ. Neyman–Pearson classification algorithms and NP receiver operating characteristics. Sci Adv. 2018;4(2):eaao1659.
  Article MATH Google Scholar
• Estabrooks A, Jo T, Japkowicz N. A multiple resampling method for learning from imbalanced data sets. Comput Intell. 2004;20(1):18–36.
  Article MathSciNet MATH Google Scholar
• Lemaître G, Nogueira F, Aridas CK. Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res. 2017;18(1):559–63.
  MATH Google Scholar
• Lin WC, Tsai CF, Hu YH, Jhang JS. Clustering-based undersampling in class-imbalanced data. Inf Sci. 2017;409:17–26.
  Article MATH Google Scholar
• Wang H, Xu Q, Zhou L. Large unbalanced credit scoring using lasso-logistic regression ensemble. PLoS ONE. 2015;10(2): e0117844.
  Article Google Scholar
• Chau VTN, Phung NH. Imbalanced educational data classification: an effective approach with resampling and random forest. In: The 2013 RIVF international conference on computing and communication technologies-research, innovation, and vision for future (RIVF). IEEE; 2013, Hanoi, Vietnam, p. 135–40.
• Song J, Lu X, Wu X. An improved AdaBoost algorithm for unbalanced classification data. In: 2009 Sixth international conference on fuzzy systems and knowledge discovery, vol. 1. IEEE; 2009, Tianjin, China, p. 109–13.
• Farquad MAH, Bose I. Preprocessing unbalanced data using support vector machine. Decis Support Syst. 2012;53(1):226–33.
  Article Google Scholar
• Tian J, Gu H, Liu W. Imbalanced classification using Euclidian distance formula ensemble. Neural Comput Appl. 2011;20(2):203–9.
  Article Google Scholar
• Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.
  Article MATH Google Scholar
• Douzas G, Bacao F, Last F. Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf Sci. 2018;465:1–20.
  Article MATH Google Scholar
• Basgall MJ, Hasperué W, Naiouf M, Fernández A, Herrera F. An analysis of local and global solutions to address big data imbalanced classification: a case study with SMOTE preprocessing. In: Conference on cloud computing and big data. Cham: Springer; 2019. p. 75–85.
• Basgall MJ, Hasperué W, Naiouf M, Fernández A, Herrera F. Smote-bd: an exact and scalable oversampling method for imbalanced classification in big data, In: Journal of Computer Science and Technology, 18(03), e23. 2018.
• Zhang W, Li X, Jia XD, Ma H, Luo Z, Li X. Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement. 2020;152: 107377.
  Article MATH Google Scholar
• Oh JH, Hong JY, Baek JG. Oversampling method using outlier detectable generative adversarial network. Expert Syst Appl. 2019;133:1–8.
  Article Google Scholar
• Demidova L, Klyueva I. SVM classification: optimization with the SMOTE algorithm for the class imbalance problem. In: 2017 6th Mediterranean conference on embedded computing (MECO). IEEE; 2017, Bar, Montenegro, p. 1–4.
• Liang XW, Jiang AP, Li T, Xue YY, Wang GT. LR-SMOTE—an improved unbalanced data set oversampling based on K-means and SVM. Knowl Based Syst. 2020;196: 105845.
  Article MATH Google Scholar
• Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M. Logistic regression. New York: Springer; 2002. p. 536.
  MATH Google Scholar
• Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565–7.
  Article MATH Google Scholar
• Batista GE, Monard MC. A study of K-nearest neighbour as an imputation method. His. 2002;87(251–260):48.
  MATH Google Scholar
• Keller JM, Gray MR, Givens JA. A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern. 1985;4:580–5.
  Article MATH Google Scholar
• Yegnanarayana B. Artificial neural networks. PHI Learning Pvt. Ltd.; 2009, New Delhi.
• Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press, 2016, headquarters office: Cambridge, Massachusetts.
• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
  Article MATH Google Scholar
• Biau G, Scornet E. A random forest guided tour. TEST. 2016;25(2):197–227.
  Article MathSciNet MATH Google Scholar
• Maier O, Wilms M, von der Gablentz J, Krämer UM, Münte TF, Handels H. Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J Neurosci Methods. 2015;240:89–100.
  Article Google Scholar
• Branco P, Torgo L, Ribeiro RP. A survey of predictive modeling on imbalanced domains. ACM Comput Surv (CSUR). 2016;49(2):1–50.
  Article MATH Google Scholar
• Wang Q, Luo Z, Huang J, Feng Y, Liu Z. A novel ensemble method for imbalanced data learning: bagging of extrapolation-SMOTE SVM. Comput Intell Neurosci. 2017.
• Fernández A, Garcia S, Herrera F, Chawla NV. SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Artif Intell Res. 2018;61:863–905.
  Article MathSciNet MATH Google Scholar
• De La Calleja J, Fuentes O. A distance-based over-sampling method for learning from imbalanced data sets. In: Proceedings of the 20th international Florida artificial intelligence research society conference, 7–9 May 2007, American Association for Artificial Intelligence, Key West, Florida, USA, p. 634–5.
• Rodríguez N, López D, Fernández A, García S, Herrera F. SOUL: Scala Oversampling and Undersampling Library for imbalance classification. SoftwareX. 2021;15: 100767.
  Article MATH Google Scholar
• Almogahed BA, Kakadiaris IA. NEATER: filtering of over-sampled data using non-cooperative game theory. Soft Comput. 2015;19(11):3301–22.
  Article MATH Google Scholar
• Bellinger C, Drummond C, Japkowicz N. Manifold-based synthetic oversampling with manifold conformance estimation. Mach Learn. 2018;107(3):605–37.
  Article MathSciNet MATH Google Scholar
• Gazzah S, Hechkel A, Amara NEB. A hybrid sampling method for imbalanced data. In: 2015 IEEE 12th international multi-conference on systems, signals, and devices (SSD15). IEEE; 2015, Mahdia, Tunisia, p. 1–6.
• Rivera WA, Goel A, Kincaid JP. OUPS: a combined approach using SMOTE and Propensity Score Matching. In: 2014 13th International conference on machine learning and applications. IEEE; 2014, Detroit, MI, USA, p. 424-7.
• Al_Janabi S, Razaq F. A novel tool DSMOTE to handel imbalance customer churn problem in telecommunication industry. In: International conference on big data and networks technologies. Cham: Springer; 2019. p. 36–50.
• Puri A, Gupta MK. Knowledge discovery from noisy imbalanced and incomplete binary class data. Expert Syst Appl. 2021;181: 115179.
  Article MATH Google Scholar
• Jiang K, Lu J, Xia K. A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE. Arab J Sci Eng. 2016;41(8):3255–66.
  Article MATH Google Scholar
• Nekooeimehr I, Lai-Yuen SK. Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets. Expert Syst Appl. 2016;46:405–16.
  Article Google Scholar
• Rivera WA. Noise reduction a priori synthetic over-sampling for class imbalanced data sets. Inf Sci. 2017;408:146–61.
  Article MATH Google Scholar
• Fernández-Navarro F, Hervás-Martínez C, Gutiérrez PA. A dynamic over-sampling procedure based on sensitivity for multi-class problems. Pattern Recognit. 2011;44(8):1821–33.
  Article MATH Google Scholar
• Farhadpour S, Warner TA, Maxwell AE. Selecting and interpreting multiclass loss and accuracy assessment metrics for classifications with class imbalance: guidance and best practices. Remote Sens. 2024;16(3):533.
  Article Google Scholar
• Nauta M, Trienes J, Pathak S, Nguyen E, Peters M, Schmitt Y, Schlötterer J, van Keulen M, Seifert C. From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI. ACM Comput Surv. 2023;55(13s):1–42.
  Article Google Scholar
• Shang H, Langlois JM, Tsioutsiouliklis K, Kang C. Precision/recall on imbalanced test data. In: International conference on artificial intelligence and statistics. PMLR; 2023, Palau de Congressos, Valencia, Spain, p. 9879–91.
• Canbek G, Taskaya Temizel T, Sagiroglu S. PToPI: a comprehensive review, analysis, and knowledge representation of binary classification performance measures/metrics. SN Comput Sci. 2022;4(1):13.
  Article MATH Google Scholar