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Creating diversity in ensembles using synthetic neighborhoods of training samples

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Abstract

Diversity among base classifiers is known to be a key driver for the construction of an effective ensemble classifier. Several methods have been proposed to construct diverse base classifiers using artificially generated training samples. However, in these methods, diversity is often obtained at the expense of the accuracy of base classifiers. Inspired by the localized generalization error model a new sample generation method is proposed in this study. When preparing different training sets for base classifiers, the proposed method generates samples located within limited neighborhoods of the corresponding training samples. The generated samples are different with the original training samples but they also expand different parts of the original training data. Learning these datasets can result in a set of base classifiers that are accurate in different regions of the input space as well as maintaining appropriate diversity. Experiments performed on 26 benchmark datasets showed that: (1) our proposed method significantly outperformed some state-of-the-art ensemble methods in term of the classification accuracy; (2) our proposed method was significantly more efficient that other sample generation based ensemble methods.

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References

  1. Brown G, Wyatt J, Harris R, Yao X (2005) Diversity creation methods: a survey and categorisation. Inf Fusion 6(1):5–20

    Article  Google Scholar 

  2. Bi Y (2012) The impact of diversity on the accuracy of evidential classifier ensembles. Int J Approx Reason 53(4):584–607

    Article  MathSciNet  Google Scholar 

  3. Sun B, Chen H, Wang J (2015) An empirical margin explanation for the effectiveness of DECORATE ensemble learning algorithm. Knowl-Based Syst 78:1–12

    Article  Google Scholar 

  4. Tsakonas A (2014) An analysis of accuracy-diversity trade-off for hybrid combined system with multiobjective predictor selection. Appl Intell 40(4):710–723

    Article  Google Scholar 

  5. Kuncheva LI (2001) Combining classifiers: soft computing solutions. Pattern recognition: from classical to modern approaches, pp 427–451

  6. Melville P, Mooney RJ (2005) Creating diversity in ensembles using artificial data. Inf Fusion 6(1):99–111

    Article  Google Scholar 

  7. Ho T (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

    Article  Google Scholar 

  8. Akhand MA, Murase K (2012) Ensembles of neural networks based on the alteration of input feature values. Int J Neural Syst 22(1):77–87

    Article  Google Scholar 

  9. Akhand MAH, Islam MM, Murase K (2009) A comparative study of data sampling techniques for constructing neural network ensembles. Int J Neural Syst 19(02):67–89

  10. Britto AS, Sabourin R, Oliveira LES (2014) Dynamic selection of classifiers—a comprehensive review. Pattern Recogn 47(11):3665–3680

    Article  Google Scholar 

  11. Yeung DS, Ng WW, Wang D, Tsang EC, et al (2007) Localized generalization error model and its application to architecture selection for radial basis function neural network. IEEE Trans Neural Netw 18(5):1294–1305

    Article  Google Scholar 

  12. Ng WWY, Dorado A, Yeung DS, Pedrycz W, et al (2007) Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error. Pattern Recogn 40 (1):19–32

    Article  MATH  Google Scholar 

  13. Ng WWY, Yeung DS, Firth M, Tsang ECC, et al (2008) Feature selection using localized generalization error for supervised classification problems using RBFNN. Pattern Recogn 41(12):3706–3719

    Article  MATH  Google Scholar 

  14. Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1-2):1–39

    Article  Google Scholar 

  15. Kotsiantis SB (2013) Bagging and boosting variants for handling classifications problems: a survey. Knowl Eng Rev 29(01):78–100

    Article  Google Scholar 

  16. Dai Q, Han XM (2016) An efficient ordering-based ensemble pruning algorithm via dynamic programming. Appl Intell 44(4):816–830

    Article  MathSciNet  Google Scholar 

  17. Ahmad A (2014) Decision tree ensembles based on kernel features. Appl Intell 41(3):855–869

    Article  Google Scholar 

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

    MATH  Google Scholar 

  19. Freund Y (1996) Experiments with a new boosting algorithm. In: Thirteenth international conference on machine learning

  20. Breiman L (2001) Random Forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  21. Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–30

    Article  Google Scholar 

  22. Freund Y, Schapire RE (1997) A Decision-Theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  MATH  Google Scholar 

  23. Xiao J, He C, Jiang X, Liu D (2010) A dynamic classifier ensemble selection approach for noise data. Inf Sci 180(18): 3402–3421

    Article  Google Scholar 

  24. Mao S, Jiao LC, Xiong L, Gou S (2011) Greedy optimization classifiers ensemble based on diversity. Pattern Recogn 44(6):1245–1261

    Article  MATH  Google Scholar 

  25. Antal B (2016) Classifier ensemble creation via false labelling. Knowl-Based Syst 89(C):278–287

    Google Scholar 

  26. Elyan E, Gaber MM (2017) A genetic algorithm approach to optimising random forests applied to class engineered data. Information Sciences 384:220–234

  27. Kuncheva LI (2013) A bound on kappa-error diagrams for analysis of classifier ensembles. IEEE Trans Knowl Data Eng 25(3):494–501

    Article  Google Scholar 

  28. Sluban B, Lavrač N (2015) Relating ensemble diversity and performance: a study in class noise detection. Neurocomputing 160:120–131

    Article  Google Scholar 

  29. Schapire RE, Freund Y, Barlett P, Lee WS (1997) Boosting the margin: a new explanation for the effectiveness of voting methods. Morgan Kaufmann Publishers Inc., pp 322–330

  30. Wang L, Sugiyama M, Jing Z, Yang C, et al (2011) A refined margin analysis for boosting algorithms via equilibrium margin. J Mach Learn Res 12(2):1835–1863

    MathSciNet  MATH  Google Scholar 

  31. Gao W, Zhou ZH (2013) On the doubt about margin explanation of boosting. Artif Intell 203(5):1–18

    Article  MathSciNet  MATH  Google Scholar 

  32. Hu Q, Li L, Wu X, Schaefer G, et al (2014) Exploiting diversity for optimizing margin distribution in ensemble learning. Knowl-Based Syst 67:90–104

    Article  Google Scholar 

  33. Li L, Zou B, Hu Q, Wu X, et al (2013) Dynamic classifier ensemble using classification confidence. Neurocomputing 99:581–591

    Article  Google Scholar 

  34. Sun B, Ng WWY, Yeung DS, Chan PPK (2013) Hyper-parameter selection for sparse LS-SVM via minimization of its localized generalization error. Int J Wavelets Multiresolution Inf Process 11(03):1350030

  35. Zhang H, Li M (2014) RWO-Sampling: a random walk over-sampling approach to imbalanced data classification. Inf Fusion 20:99–116

    Article  Google Scholar 

  36. Asuncion A (2007) And D. UCI machine learning repository, Newman

    Google Scholar 

  37. Alcalá-Fdez J, Sánchez L, García S, del Jesus MJ , et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

    Article  Google Scholar 

  38. Hall M, Frank E, Holmes G, Pfahringer B, et al (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  39. Chang CC, Lin CJ (2007) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2(3, article 27):389–396

    Google Scholar 

  40. Mukherjee I, Schapire RE (2011) A theory of multiclass boosting. J Mach Learn Res 14(1):437–497

    MathSciNet  MATH  Google Scholar 

  41. Dem and J. Ar (2006) Statistical Comparisons of Classifiers over Multiple Data Sets. J Mach Learn Res 7(1):1–30

  42. Hodges JL, Lehmann EL (1962) Rank methods for combination of independent experiments in analysis of variance. Ann Math Stat 33(2):482–497

    Article  MathSciNet  MATH  Google Scholar 

  43. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70

    MathSciNet  MATH  Google Scholar 

  44. Tang EK, Suganthan PN, Yao X (2006) An analysis of diversity measures. Mach Learn 65(1):247–271

    Article  Google Scholar 

  45. Tsymbal A, Pechenizkiy M, Cunningham P (2005) Diversity in search strategies for ensemble feature selection. Inf Fusion 6(1):83–98

    Article  Google Scholar 

  46. Dai Q (2013) A competitive ensemble pruning approach based on cross-validation technique. Knowl-Based Syst 37(2):394–414

    Article  Google Scholar 

  47. Dai Q, Yao CS (2016) A hierarchical and parallel branch-and-bound ensemble selection algorithm. Appl Intell:1–17

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, Sichuan, China

    Zhi Chen, Tao Lin, Rui Chen, Yingtao Xie & Hongyan Xu

  2. Sichuan University, No.24 South Section 1 of Yihuan Road, Chengdu, Sichuan province, China

    Tao Lin

Authors
  1. Zhi Chen
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  2. Tao Lin
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  3. Rui Chen
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  4. Yingtao Xie
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  5. Hongyan Xu
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Correspondence to Tao Lin.

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Chen, Z., Lin, T., Chen, R. et al. Creating diversity in ensembles using synthetic neighborhoods of training samples. Appl Intell 47, 570–583 (2017). https://doi.org/10.1007/s10489-017-0922-3

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