Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics

Abstract

One of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheuristic called GACE is used to optimize the different parameters related to the feature space partitioning. To assess the performance of the proposal, an extensive experimentation over imbalanced and real-world datasets compares different configurations and base classifiers. Its performance is competitive with that of reference techniques in the literature.

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Fig. 1

Notes

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    http://sci2s.ugr.es/keel/datasets.php

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    http://www.ict-icsi.eu/

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    prtools.org/prtools/prtools-overview/

References

  1. 1.

    Alcala-Fdez J, Alcala R, Herrera F (2011) A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans Fuzzy Syst 19(5):857–872

    Article  Google Scholar 

  2. 2.

    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 Multiple-Valued Logic Soft Comput 17(2–3):255–287

    Google Scholar 

  3. 3.

    Amami R, Ben Ayed D, Ellouze N (2013) Adaboost with SVM using GMM supervector for imbalanced phoneme data. In: 2013 The 6th international conference on human system interaction (HSI), pp 328–333

  4. 4.

    Bäck T, Schwefel H (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1– 23

    Article  Google Scholar 

  5. 5.

    Bi Y, Guan J, Bell D (2008) The combination of multiple classifiers using an evidential reasoning approach. Artif Intell 172(15):1731–1751

    MATH  Article  Google Scholar 

  6. 6.

    Bian J, Peng XG, Wang Y, Zhang H (2016) An efficient cost-sensitive feature selection using chaos genetic algorithm for class imbalance problem. Math Probl Eng, 2016

  7. 7.

    Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2 (2):121–167

    Article  Google Scholar 

  8. 8.

    Cervantes J, Huang DS, García-Lamont F, Chau A (2014) A hybrid algorithm to improve the accuracy of support vector machines on skewed data-sets. In: International conference on intelligent computing, pp 782–788

  9. 9.

    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. Springer, pp 107–119

  10. 10.

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

    MATH  Article  Google Scholar 

  11. 11.

    Danesh A, Moshiri B, Fatemi O (2007) Improve text classification accuracy based on classifier fusion methods. In: 10th International conference on information fusion, pp 1–6

  12. 12.

    Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  13. 13.

    Díez-Pastor JF, Rodríguez GOCJ, Kuncheva LIJ (2015) Random balance: ensembles of variable priors classifiers for imbalanced data. Knowl-Based Syst 85:96–111

    Article  Google Scholar 

  14. 14.

    Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  15. 15.

    Duin RP (2002) The combining classifier: to train or not to train? In: Proceedings 16th international conference patter recognition, vol 2. IEEE, pp 765–770

  16. 16.

    Eshelman LJ, Schaffer JD (1992) Real-coded genetic algorithms and interval-schemata. Found Gen Algor 2:187–202

    Google Scholar 

  17. 17.

    Fattahi S, Othman Z, Othman Z (2015) New approach with ensemble method to address class imbalance problem. J Theor Appl Inf Technol 72:1

    Google Scholar 

  18. 18.

    Finner H (1993) On a monotonicity problem in step-down multiple test procedures. J Am Stat Assoc 88 (423):920–923

    MathSciNet  MATH  Article  Google Scholar 

  19. 19.

    Giacinto G, Roli F (2001) Dynamic classifier selection based on multiple classifier behaviour. Pattern Recogn 34(9):1879– 1881

    MATH  Article  Google Scholar 

  20. 20.

    Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. Found Gen Algor 1:69–93

    MathSciNet  Google Scholar 

  21. 21.

    Haixiang G, Xiuwu L, Kejun Z, Chang D, Yanhui G (2011) Optimizing reservoir features in oil exploration management based on fusion of soft computing. Appl Soft Comput 11(1):1144–1155

    Article  Google Scholar 

  22. 22.

    Hashem S (1997) Optimal linear combinations of neural networks. Neural Netw 10(4):599–614

    MathSciNet  Article  Google Scholar 

  23. 23.

    Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12(4):265–319

    MATH  Article  Google Scholar 

  24. 24.

    Ho D, Drake T, Bentley R, Valea F, Wax A (2015) Evaluation of hybrid algorithm for analysis of scattered light using ex vivo nuclear morphology measurements of cervical epithelium. Biom Opt Express 6 (8):2755–2765

    Article  Google Scholar 

  25. 25.

    Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press

  26. 26.

    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    MathSciNet  MATH  Google Scholar 

  27. 27.

    Hopfield J (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(8):2554–2558

    MathSciNet  MATH  Article  Google Scholar 

  28. 28.

    Jackowski K, Wozniak M (2009) Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Anal Applic 12(4):415–425

    MathSciNet  Article  Google Scholar 

  29. 29.

    Jackowski K, Krawczyk B, Woźniak M (2014) Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning. Int J Neural Syst 24(03):1430007

    Article  Google Scholar 

  30. 30.

    Jackowski K (2015) Adaptive splitting and selection algorithm for regression. N Gener Comput 33(4):425–448

    Article  Google Scholar 

  31. 31.

    del Jesus M, Hoffmann F, Junco L, Sánchez L (2004) Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans Fuzzy Syst 12(3):296–308

    Article  Google Scholar 

  32. 32.

    Jurek A, Bi Y, Wu S, Nugent C (2011) Classification by cluster analysis: a new meta-learning based approach. Multiple Classif Syst, 259–268

  33. 33.

    Jurek A, Bi Y, Wu S, Nugent C (2014) A survey of commonly used ensemble-based classification techniques. Knowl Eng Rev 29(5):551–581

    Article  Google Scholar 

  34. 34.

    Kennedy J (2011) Particle swarm optimization. Encyclopedia of machine learning. Springer, pp 760–766

  35. 35.

    Krawczyk B, Cyganek B (2017) Selecting locally specialised classifiers for one-class classification ensembles. Pattern Anal Appl 20(2):427–439

    MathSciNet  Article  Google Scholar 

  36. 36.

    Krawczyk B, McInnes BT (2018) Local ensemble learning from imbalanced and noisy data for word sense disambiguation. Pattern Recogn 78:103–119

    Article  Google Scholar 

  37. 37.

    Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley

  38. 38.

    Kuncheva LI, Jain LC (2000) Designing classifier fusion systems by genetic algorithms. IEEE Trans Evol Comput 4(4):327–336

    Article  Google Scholar 

  39. 39.

    Kuncheva LI, Whitaker CJ, Shipp CA, Duin RP (2003) Limits on the majority vote accuracy in classifier fusion. Pattern Anal Appl 6(1):22–31

    MathSciNet  MATH  Article  Google Scholar 

  40. 40.

    Lavanya S, Palaniswami S, Divyabharathi M (2015) Resampling ensemble algorithm for class imbalance problem using optimization algorithm. Int J Appl Eng Res 10(13):11520–11526

    Google Scholar 

  41. 41.

    Liu X, Lin J, Deng K (2011) Scheduling optimization in re-entrant lines based on a GA and PSO hybrid algorithm. Tongji Daxue Xuebao/J Tongji Univ 39:726–729

    MATH  Google Scholar 

  42. 42.

    Lopez-Garcia P, Onieva E, Osaba E, Masegosa A, Perallos A (2016) Gace: a meta-heuristic based in the hybridization of genetic algorithms and cross entropy methods for continuous optimization. Expert Syst Appl 55:508–519

    Article  Google Scholar 

  43. 43.

    Lopez-Garcia P, Onieva E, Osaba E, Masegosa AD, Perallos A (2016) A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans Intell Transp Syst 17(2):557–569

    Article  Google Scholar 

  44. 44.

    Lopez-Garcia P, Woźniak M, Onieva E, Perallos A (2016c) Hybrid optimization method applied to adaptive splitting and selection algorithm. Lecture notes in computer science, vol 9648. Springer, pp 742–750

  45. 45.

    Mauša G, Galinac Grbac T (2017) Co-evolutionary multi-population genetic programming for classification in software defect prediction: an empirical case study. Appl Soft Comput J 55:331–351

    Article  Google Scholar 

  46. 46.

    Mokeddem D, Belbachir H (2009) A survey of distributed classification based ensemble data mining methods. J Appl Sci 9(20):3739–3745

    Article  Google Scholar 

  47. 47.

    Opitz DW, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198

    MATH  Article  Google Scholar 

  48. 48.

    Paredes R, Vidal E (2006) Learning weighted metrics to minimize nearest-neighbor classification error. IEEE Trans Pattern Anal Mach Intell 28(7):1100–1110

    Article  Google Scholar 

  49. 49.

    Qian Y, Liang Y, Li M, Feng G, Shi X (2014) A resampling ensemble algorithm for classification of imbalance problems. Neurocomputing 143:57–67

    Article  Google Scholar 

  50. 50.

    Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1):1–39

    MathSciNet  Article  Google Scholar 

  51. 51.

    Ruta D, Gabrys B (2005) Classifier selection for majority voting. Inform Fus 6(1):63–81

    MATH  Article  Google Scholar 

  52. 52.

    Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2010) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern-Part A: Syst Humans 40(1):185–197

    Article  Google Scholar 

  53. 53.

    Sentinella M, Casalino L (2009) Cooperative evolutionary algorithm for space trajectory optimization. Celest Mech Dyn Astron 105(1-3):211

    MathSciNet  MATH  Article  Google Scholar 

  54. 54.

    Stanciu S, Tranca D, Stanciu G, Hristu R, Bueno J (2016) Perspectives on combining nonlinear laser scanning microscopy and bag-of-features data classification strategies for automated disease diagnostics. Opt Quant Electron 48(6):320

    Article  Google Scholar 

  55. 55.

    Vorraboot P, Rasmequan S, Chinnasarn K, Lursinsap C (2015) Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms. Neurocomputing 152:429–443

    Article  Google Scholar 

  56. 56.

    Wang S, Yao X (2009) Diversity analysis on imbalanced data sets by using ensemble models. In: Proceedings of IEEE symposium in computational intelligence and data mining, 2009, CIDM’09, pp 324–331

  57. 57.

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

    Article  Google Scholar 

  58. 58.

    Wang S, Minku L, Yao X (2015) Resampling-based ensemble methods for online class imbalance learning. IEEE Trans Knowl Data Eng 27(5):1356–1368

    Article  Google Scholar 

  59. 59.

    Xu L, Krzyzak A, Suen CY (1992) Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern 22(3):418–435

    Article  Google Scholar 

  60. 60.

    Yang J, Ji Z, Xie W, Zhu Z (2016) Model selection based on particle swarm optimization for omics data classification. Shenzhen Daxue Xuebao (Ligong Ban)/J Shenzhen Univ Sci Eng 33(3):264–271

    Article  Google Scholar 

  61. 61.

    Yang P, Xu L, Zhou B, Zhang Z, Zomaya A (2009) A particle swarm based hybrid system for imbalanced medical data sampling. BMC Genomics 10:Suppl. 3. https://doi.org/10.1186/1471-2164-10-S3-S34

    Article  Google Scholar 

  62. 62.

    Yang XS (2010) A new metaheuristic bat-inspired algorithm. Stud Comput Intell 284:65–74

    MATH  Google Scholar 

  63. 63.

    Yu H, Ni J, Zhao J (2013) ACOSampling: an ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data. Neurocomputing 101:309–318

    Article  Google Scholar 

  64. 64.

    Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63–77

    MathSciNet  Article  Google Scholar 

  65. 65.

    Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Progress Artif Intell 5(4):221–232

    Article  Google Scholar 

  66. 66.

    Cano A, Zafra A, Ventura S (2013) Weighted data gravitation classification for standard and imbalanced data. IEEE Trans Cybern 43(6):1672–1687

    Article  Google Scholar 

  67. 67.

    Mahdizadehaghdam S, Dai L, Krim H, Skau E, Wang H (2017) Image classification: a hierarchical dictionary learning approach. In: IEEE International conference in acoustics, speech and signal processing (ICASSP), 2017, pp 2597–2601

  68. 68.

    Khari M, Kumar P, Burgos D, Crespo RG (2017) Optimized test suites for automated testing using different optimization techniques. Soft Comput, 1–12

  69. 69.

    Fernández A, García S, Herrera F (2011) Addressing the classification with imbalanced data: open problems and new challenges on class distribution. Hybrid Artif Intell Syst, 1–10

  70. 70.

    Sun Y, Wong AK, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(04):687–719

    Article  Google Scholar 

  71. 71.

    Krawczyk B, Cano A, Woźniak M (2018) Selecting local ensembles for multi-class imbalanced data classification, In: 2018 International joint conference on neural networks (IJCNN) 1–8

  72. 72.

    Fernandez A, Garcia S, Galar M, Prati RC, Krawczyk B, Herrera F (2018) Learning from imbalanced data sets. Springer

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Acknowledgments

This work has been supported by the research projects TEC2013-45585-C2-2-R and TIN2014-56042-JIN from the Spanish Ministry of Economy and Competitiveness, the TIMON project, which received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No. 636220, and the LOGISTAR project, which received funding from European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769142.

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Correspondence to Pedro Lopez-Garcia.

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Lopez-Garcia, P., Masegosa, A.D., Osaba, E. et al. Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics. Appl Intell 49, 2807–2822 (2019). https://doi.org/10.1007/s10489-019-01423-6

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Keywords

  • Ensemble classification
  • Imbalanced classification
  • Feature space partitioning
  • Hybrid metaheuristics