Skip to main content
SpringerLink
Log in

ESMOTE: an overproduce-and-choose synthetic examples generation strategy based on evolutionary computation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The class imbalance learning problem is an important topic that has attracted considerable attention in machine learning and data mining. The most common method of addressing imbalanced datasets is the synthetic minority oversampling technique (SMOTE). However, the SMOTE and its variants suffer from the noise derived from the interpolation of synthetic examples. In this paper, an overproduce-and-choose strategy, which is divided into the overproduction and selection phases, is proposed to generate an appropriate set of synthetic examples for imbalance learning problems. In the overproduction phase, a new interpolation mechanism is developed to produce numerous synthetic examples, while in the selection phase, the synthetic examples that are beneficial to the classification task are selected by using instance selection based on evolutionary computation. Experiments are conducted on a large number of datasets selected from the real-world applications. The experimental results demonstrate that the proposed method is significantly better than SMOTE and its well-known variants in terms of several metrics, including G-mean (GM) and area under the curve.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

All the research data presented in this work can be reached when contacting with the author by email

References

  1. Wang S, Yao X (2012) Multiclass imbalance problems: analysis and potential solutions. IEEE Trans Syst Man Cybern Part B (Cybern) 42(4):1119–1130

    Google Scholar 

  2. Fernández A, LóPez V, Galar M, Del Jesus MJ, Herrera F (2013) Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches. Knowl-Based Syst 42:97–110

    Google Scholar 

  3. Hou W-H, Wang X-K, Zhang H-Y, Wang J-Q, Li L (2020) A novel dynamic ensemble selection classifier for an imbalanced data set: an application for credit risk assessment. Knowl-Based Syst 208:106462

    Google Scholar 

  4. Choudhary R, Shukla S (2021) A clustering based ensemble of weighted kernelized extreme learning machine for class imbalance learning. Expert Syst Appl 164:114041

    Google Scholar 

  5. Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2011) A review on ensembles for the class imbalance problem: bagging, boosting, and hybrid-based approaches. IEEE Transact Syst Man Cybern Part C (Appl Rev) 42(4):463–484

    Google Scholar 

  6. Fernández A, Garcia S, Herrera F, Chawla NV (2018) SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Artif Intell Res 61:863–905

    MathSciNet  MATH  Google Scholar 

  7. Datta S, Das S (2015) Near-Bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs. Neural Netw 70:39–52

    MATH  Google Scholar 

  8. Ren Z, Zhu Y, Kang W, Fu H, Niu Q, Gao D, Yan K, Hong J (2022) Adaptive cost-sensitive learning: improving the convergence of intelligent diagnosis models under imbalanced data. Knowl-Based Syst 241:108296

    Google Scholar 

  9. Guzmán-Ponce A, Sánchez JS, Valdovinos RM, Marcial-Romero JR (2021) DBIG-US: a two-stage under-sampling algorithm to face the class imbalance problem. Expert Syst Appl 168:114301

    Google Scholar 

  10. Chen Z, Duan J, Kang L, Qiu G (2021) A hybrid data-level ensemble to enable learning from highly imbalanced dataset. Inf Sci 554:157–176

    MathSciNet  MATH  Google Scholar 

  11. Barella VH, Garcia LP, de Souto MC, Lorena AC, de Carvalho AC (2021) Assessing the data complexity of imbalanced datasets. Inf Sci 553:83–109

    MathSciNet  MATH  Google Scholar 

  12. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    MATH  Google Scholar 

  13. Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult-Valued Log Soft Comput 17:255–287

    Google Scholar 

  14. Barandela R, Valdovinos RM, Sánchez JS (2003) New applications of ensembles of classifiers. Pattern Anal Appl 6(3):245–256

    MathSciNet  Google Scholar 

  15. López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113–141

    Google Scholar 

  16. Gosain A, Sardana S (2019) Farthest SMOTE: a modified SMOTE approach. In: Computational intelligence in data mining. Springer, pp 309–320

  17. García V, Sánchez JS, Martín-Félez R, Mollineda RA (2012) Surrounding neighborhood-based SMOTE for learning from imbalanced data sets. Progr Artif Intell 1(4):347–362

    Google Scholar 

  18. Dietterich TG, Bakiri G (1994) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2:263–286

    MATH  Google Scholar 

  19. López V, Fernández A, Herrera F (2014) On the importance of the validation technique for classification with imbalanced datasets: addressing covariate shift when data is skewed. Inf Sci 257:1–13

    Google Scholar 

  20. Weiss GM (2004) Mining with rarity: a unifying framework. ACM SIGKDD Explor Newsl 6(1):7–19

    MathSciNet  Google Scholar 

  21. Weiss GM, Tian Y (2008) Maximizing classifier utility when there are data acquisition and modeling costs. Data Min Knowl Disc 17(2):253–282

    MathSciNet  Google Scholar 

  22. Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6(1):20–29

    Google Scholar 

  23. Cieslak DA, Hoens TR, Chawla NV, Kegelmeyer WP (2012) Hellinger distance decision trees are robust and skew-insensitive. Data Min Knowl Disc 24(1):136–158

    MathSciNet  MATH  Google Scholar 

  24. Czarnecki WM, Tabor J (2015) Multithreshold entropy linear classifier: theory and applications. Expert Syst Appl 42(13):5591–5606

    Google Scholar 

  25. Ando S (2016) Classifying imbalanced data in distance-based feature space. Knowl Inf Syst 46(3):707–730

    Google Scholar 

  26. Pérez-Godoy MD, Rivera AJ, Carmona CJ, del Jesus MJ (2014) Training algorithms for radial basis function networks to tackle learning processes with imbalanced data-sets. Appl Soft Comput 25:26–39

    Google Scholar 

  27. Penar W, Wozniak M (2010) Cost-sensitive methods of constructing hierarchical classifiers. Expert Syst 27(3):146–155

    Google Scholar 

  28. Zhou Z-H, Liu X-Y (2010) On multi-class cost-sensitive learning. Comput Intell 26(3):232–257

    MathSciNet  Google Scholar 

  29. Sun Y, Kamel MS, Wong AK, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn 40(12):3358–3378

    MATH  Google Scholar 

  30. Fan W, Stolfo SJ, Zhang J, Chan PK (1999) AdaCost: misclassification cost-sensitive boosting. In: Icml, vol 99, pp 97–105

  31. Ting KM (2000) A comparative study of cost-sensitive boosting algorithms. In: In Proceedings of the 17th International Conference on Machine Learning . Citeseer

  32. Zhou Z-H, Liu X-Y (2005) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63–77

    MathSciNet  Google Scholar 

  33. Drummond C, Holte RC (2000) Exploiting the cost (in) sensitivity of decision tree splitting criteria. In: ICML, vol 1(1), 239–246

  34. Krawczyk B, Woźniak M, Schaefer G (2014) Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl Soft Comput 14:554–562

    Google Scholar 

  35. López V, Del Río S, Benítez JM, Herrera F (2015) Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data. Fuzzy Sets Syst 258:5–38

    MathSciNet  Google Scholar 

  36. Woźniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inform Fus 16:3–17

    Google Scholar 

  37. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MATH  Google Scholar 

  38. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: ICML, vol 96, Citeseer, pp 148–156

  39. Wang S, Yao X (2009) Diversity analysis on imbalanced data sets by using ensemble models. In: 2009 IEEE symposium on computational intelligence and data mining, pp 324–331

  40. Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery, pp. 107–119

  41. Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2009) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern-Part A Syst Humans 40(1):185–197

    Google Scholar 

  42. Galar M, Fernández A, Barrenechea E, Herrera F (2013) EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recogn 46(12):3460–3471

    Google Scholar 

  43. Liu X-Y, Wu J, Zhou Z-H (2008) Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern Part B (Cybern) 39(2):539–550

    Google Scholar 

  44. Nanni L, Fantozzi C, Lazzarini N (2015) Coupling different methods for overcoming the class imbalance problem. Neurocomputing 158:48–61

    Google Scholar 

  45. Sáez JA, Luengo J, Stefanowski J, Herrera F (2015) SMOTE-IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf Sci 291:184–203

    Google Scholar 

  46. He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE World Congress on Computational Intelligence), pp 1322–1328

  47. Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf Sci 465:1–20

    Google Scholar 

  48. Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: International conference on intelligent computing, pp 878–887

  49. Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-Level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Pacific-Asia conference on knowledge discovery and data mining, pp 475–482

  50. Maciejewski T, Stefanowski J (2011) Local neighbourhood extension of SMOTE for mining imbalanced data. In: 2011 IEEE symposium on computational intelligence and data mining (CIDM), pp 104–111

  51. Yun J, Ha J, Lee J-S (2016) Automatic determination of neighborhood size in SMOTE. In: Proceedings of the 10th international conference on ubiquitous information management and communication, pp 1–8

  52. Ziȩba M, Tomczak JM, Gonczarek A (2015) RBM-SMOTE: restricted boltzmann machines for synthetic minority oversampling technique. In: Asian conference on intelligent information and database systems, pp 377–386

  53. Wang K-J, Adrian AM, Chen K-H, Wang K-M (2015) A hybrid classifier combining Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: A case study in taiwan. Comput Methods Programs Biomed 119(2):63–76

    Google Scholar 

  54. Raghuwanshi BS, Shukla S (2020) SMOTE based class-specific extreme learning machine for imbalanced learning. Knowl-Based Syst 187:104814

    Google Scholar 

  55. Randall D, Tony W, Martinez R (2000) Reduction techniques for Exemplar-Based learning algorithms. Mach Learn 38(3):257–286

    MATH  Google Scholar 

  56. García S, Luengo J, Herrera F (2016) Data preprocessing in data mining. Springer Publishing Company Incorporated, Heidelberg

    Google Scholar 

  57. Hart PE (1968) The condensed nearest neighbor rule. IEEE Trans Inf Theory 14(3):515–516

    Google Scholar 

  58. Olvera-López JA, Carrasco-Ochoa JA, Martínez-Trinidad JF (2010) A new fast prototype selection method based on clustering. Pattern Anal Appl 13(2):131–141

    MathSciNet  Google Scholar 

  59. Wilson Dennis L (2007) Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern 2(3):408–421

    MathSciNet  MATH  Google Scholar 

  60. Sánchez JS, Pla F, Ferri FJ (1997) Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recogn Lett 18(6):507–513

    Google Scholar 

  61. Vázquez F, Sánchez JS, Pla F (2005) A stochastic approach to Wilson’s editing algorithm. In: Iberian conference on pattern recognition and image analysis, pp 35–42

  62. Sánchez JS, Barandela R, Marqués AI, Alejo R, Badenas J (2003) Analysis of new techniques to obtain quality training sets. Pattern Recogn Lett 24(7):1015–1022

    Google Scholar 

  63. Jankowski N, Grochowski M (2004) Comparison of instances seletion algorithms I. Algorithms survey. Curr Gastroenterol Rep 10(1):0937–0942

    MATH  Google Scholar 

  64. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66

    Google Scholar 

  65. Marchiori E (2009) Class conditional nearest neighbor for large margin instance selection. IEEE Trans Pattern Anal Mach Intell 32(2):364–370

    Google Scholar 

  66. Brighton H, Mellish C (2002) Advances in instance selection for instance-based learning algorithms. Data Min Knowl Disc 6(2):153–172

    MathSciNet  MATH  Google Scholar 

  67. Zhao K-P, Zhou S-G, Guan J-H, Zhou A-Y (2003) C-pruner: an improved instance pruning algorithm. In: Proceedings of the 2003 international conference on machine learning and cybernetics (IEEE Cat. No. 03EX693), vol. 1, pp 94–99

  68. Cano JR, Herrera F, Lozano M (2003) Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. IEEE Trans Evol Comput 7(6):561–575

    Google Scholar 

  69. Tsai CF, Eberle W, Chu CY (2013) Genetic algorithms in feature and instance selection. Knowl-Based Syst 39:240–247

    Google Scholar 

  70. Suganthi M, Karunakaran V (2019) Instance selection and feature extraction using cuttlefish optimization algorithm and principal component analysis using decision tree. Clust Comput 22(1):89–101

    Google Scholar 

  71. Rathee S, Ratnoo S, Ahuja J (2019) Instance selection using multi-objective CHC evolutionary algorithm. In: Information and communication technology for competitive strategies, pp 475–484

  72. Kuncheva LI (1995) Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recogn Lett 16(8):809–814

    Google Scholar 

  73. Sierra B, Lazkano E, Inza I, Merino M, Larranaga P, Quiroga J (2001) Prototype selection and feature subset selection by estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with tips. In: Conference on artificial intelligence in medicine in Europe, pp 20–29

  74. Loh W-Y (2011) Classification and regression trees. Wiley Interdis Rev Data Min Knowl Disc 1(1):14–23

    Google Scholar 

  75. Quinlan JR (2014) C4. 5: programs for machine learning, Elsevier

  76. Rokach L (2016) Decision forest: twenty years of research. Inform Fus 27:111–125

    Google Scholar 

  77. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  78. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    MATH  Google Scholar 

  79. Wright RE (1995) Logistic regression. Reading and Underst Multivar Stat:217–244

  80. Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29(2):103–130

    MATH  Google Scholar 

  81. Barua S, Islam MM, Yao X, Murase K (2012) MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405–425

    Google Scholar 

  82. Rong T, Gong H, Ng WW (2014) Stochastic sensitivity oversampling technique for imbalanced data. In: International conference on machine learning and cybernetics, pp 161–171

  83. Menardi G, Torelli N (2014) Training and assessing classification rules with imbalanced data. Data Min Knowl Disc 28(1):92–122

    MathSciNet  MATH  Google Scholar 

  84. Jiang K, Lu J, Xia K (2016) A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE. Arab J Sci Eng 41(8):3255–3266

    Google Scholar 

  85. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    MathSciNet  Google Scholar 

  86. Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures, Chapman and Hall/CRC

  87. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    MATH  Google Scholar 

  88. Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86–92

    MathSciNet  MATH  Google Scholar 

  89. Wilcoxon F (1992) Individual comparisons by ranking methods. In: Breakthroughs in statistics, Chapman and Hall/CRC, pp 196–202

Download references

Acknowledgements

The authors would like to thank the (anonymous) reviewers for their constructive comments. This research was supported by the National Science Foundation of China under Grant No. 71801065, 72171065, 71831006 and 71801064, Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ20G010001 and LY22G010009, as well as the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant No. GK209907299001-202.

Author information

Authors and Affiliations

  1. School of Management, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zhong-Liang Zhang, Rui-Rui Peng, Yuan-Peng Ruan, Jian Wu & Xing-Gang Luo

Authors
  1. Zhong-Liang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui-Rui Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuan-Peng Ruan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xing-Gang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Wu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A Detailed tables of experimental results

Appendix A Detailed tables of experimental results

In this appendix, the detailed experimental results in all the base classifiers and datasets are presented. These results are as follows:

See Tables

Table 10 The mean and standard deviation results of GM for each method on DT classifier
Full size table

10,

Table 11 The mean and standard deviation results of AUC for each method on DT classifier
Full size table

11,

Table 12 The mean and standard deviation results of GM for each method on NB classifier
Full size table

12,

Table 13 The mean and standard deviation results of AUC for each method on NB classifier
Full size table

13,

Table 14 The mean and standard deviation results of GM for each method on LR classifier
Full size table

14,

Table 15 The mean and standard deviation results of AUC for each method on LR classifier
Full size table

15,

Table 16 The mean and standard deviation results of GM for each method on KNN classifier
Full size table

17,

Table 17 The mean and standard deviation results of AUC for each method on KNN classifier
Full size table

17,

Table 18 The mean and standard deviation results of GM for each method on SVM classifier
Full size table

18,

Table 19 The mean and standard deviation results of AUC for each method on SVM classifier
Full size table

and 19.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, ZL., Peng, RR., Ruan, YP. et al. ESMOTE: an overproduce-and-choose synthetic examples generation strategy based on evolutionary computation. Neural Comput & Applic 35, 6891–6977 (2023). https://doi.org/10.1007/s00521-022-08004-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08004-8

Keywords

Search

Navigation