메뉴 건너뛰기




Volumn 8, Issue 5, 2018, Pages

Class imbalance ensemble learning based on the margin theory

Author keywords

Classification; Ensemble learning; Ensemble margin; Imbalance learning; Multi class

Indexed keywords


EID: 85047080680     PISSN: None     EISSN: 20763417     Source Type: Journal    
DOI: 10.3390/app8050815     Document Type: Article
Times cited : (132)

References (78)
  • 1
    • 27944460950 scopus 로고    scopus 로고
    • Total margin based adaptive fuzzy support vector machines for multiview face recognition
    • Man and Cybernetics, Waikoloa, HI, USA, 10-12 October
    • Liu, Y.H.; Chen, Y.T. Total margin based adaptive fuzzy support vector machines for multiview face recognition. In Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, USA, 10-12 October 2005; Volume 2, pp. 1704-1711
    • (2005) Proceedings of the 2005 IEEE International Conference on Systems , vol.2 , pp. 1704-1711
    • Liu, Y.H.1    Chen, Y.T.2
  • 2
    • 84977676336 scopus 로고    scopus 로고
    • Self-training in significance space of support vectors for imbalanced biomedical event data
    • Munkhdalai, T.; Namsrai, O.E.; Ryu, K.H. Self-training in significance space of support vectors for imbalanced biomedical event data. BMC Bioinform. 2015, 16, 1-8
    • (2015) BMC Bioinform , vol.16 , pp. 1-8
    • Munkhdalai, T.1    Namsrai, O.E.2    Ryu, K.H.3
  • 3
    • 84925452064 scopus 로고    scopus 로고
    • Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem
    • Siers, M.; Islam, M.Z. Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem. Inf. Syst. 2015, 51, 62-71
    • (2015) Inf. Syst , vol.51 , pp. 62-71
    • Siers, M.1    Islam, M.Z.2
  • 4
    • 84907442805 scopus 로고    scopus 로고
    • Imbalanced Hyperspectral Image Classification Based on Maximum Margin
    • Sun, T.; Jiao, L.; Feng, J.; Liu, F.; Zhang, X. Imbalanced Hyperspectral Image Classification Based on Maximum Margin. IEEE Geosci. Remote Sens. Lett. 2015, 12, 522-526
    • (2015) IEEE Geosci. Remote Sens. Lett , vol.12 , pp. 522-526
    • Sun, T.1    Jiao, L.2    Feng, J.3    Liu, F.4    Zhang, X.5
  • 7
    • 33845536164 scopus 로고    scopus 로고
    • The Class Imbalance Problem: A Systematic Study
    • Japkowicz, N.; Stephen, S. The Class Imbalance Problem: A Systematic Study. Intell. Data Anal. 2002, 6, 429-449
    • (2002) Intell. Data Anal , vol.6 , pp. 429-449
    • Japkowicz, N.1    Stephen, S.2
  • 9
    • 85043605198 scopus 로고    scopus 로고
    • Learning from imbalanced data: Open challenges and future directions
    • Krawczyk, B. Learning from imbalanced data: Open challenges and future directions. Prog. Artif. Intell. 2016, 5, 221-232
    • (2016) Prog. Artif. Intell , vol.5 , pp. 221-232
    • Krawczyk, B.1
  • 10
    • 84979464666 scopus 로고    scopus 로고
    • Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
    • Sáez, J.A.; Krawczyk, B.;Wózniak, M. Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets. Pattern Recognit. 2016, 57, 164-178
    • (2016) Pattern Recognit , vol.57 , pp. 164-178
    • Sáez, J.A.1    Krawczyk, B.2    Wózniak, M.3
  • 12
    • 70349617264 scopus 로고    scopus 로고
    • Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
    • García, S.; Herrera, F. Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy. Evol. Comput. 2009, 17, 275-306
    • (2009) Evol. Comput , vol.17 , pp. 275-306
    • García, S.1    Herrera, F.2
  • 13
    • 77949543086 scopus 로고    scopus 로고
    • Cost-sensitive Learning and the Class Imbalanced Problem
    • Sammut, C., Ed.; Springer: Berlin, Germany
    • Ling, C.X.; Sheng, V.S. Cost-sensitive Learning and the Class Imbalanced Problem. In Encyclopedia of Machine Learning; Sammut, C., Ed.; Springer: Berlin, Germany, 2008
    • (2008) Encyclopedia of Machine Learning
    • Ling, C.X.1    Sheng, V.S.2
  • 15
    • 31344442851 scopus 로고    scopus 로고
    • Training cost-sensitive neural networks with methods addressing the class imbalance problem
    • Zhou, Z.; Liu, X.Y. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 2006, 18, 63-77
    • (2006) IEEE Trans. Knowl. Data Eng , vol.18 , pp. 63-77
    • Zhou, Z.1    Liu, X.Y.2
  • 16
    • 34547673383 scopus 로고    scopus 로고
    • Cost-sensitive boosting for classification of imbalanced data
    • Sun, Y.; Kamel, M.S.; Wong, A.K.; Wang, Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit. 2007, 40, 3358-3378
    • (2007) Pattern Recognit , vol.40 , pp. 3358-3378
    • Sun, Y.1    Kamel, M.S.2    Wong, A.K.3    Wang, Y.4
  • 19
    • 27144489164 scopus 로고    scopus 로고
    • A Tutorial on Support Vector Machines for Pattern Recognition
    • Burges, C.J.C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Discov. 1998, 2, 121-167
    • (1998) Data Min. Knowl. Discov , vol.2 , pp. 121-167
    • Burges, C.J.C.1
  • 21
    • 79957964024 scopus 로고    scopus 로고
    • Margin-Based Over-Sampling Method for Learning from Imbalanced Datasets
    • Springer: Berlin/Heidelberg, Germany
    • Fan, X.N.; Tang, K.; Weise, T. Margin-Based Over-Sampling Method for Learning from Imbalanced Datasets. In Advances in Knowledge Discovery and Data Mining; Springer: Berlin/Heidelberg, Germany, 2011; Volume 6635, pp. 309-320
    • (2011) Advances in Knowledge Discovery and Data Mining , vol.6635 , pp. 309-320
    • Fan, X.N.1    Tang, K.2    Weise, T.3
  • 22
    • 84904800514 scopus 로고    scopus 로고
    • A resampling ensemble algorithm for classification of imbalance problems
    • Qian, Y.; Liang, Y.; Li, M.; Feng, G.; Shi, X. A resampling ensemble algorithm for classification of imbalance problems. Neurocomputing 2014, 143, 57-67
    • (2014) Neurocomputing , vol.143 , pp. 57-67
    • Qian, Y.1    Liang, Y.2    Li, M.3    Feng, G.4    Shi, X.5
  • 23
    • 70450182721 scopus 로고    scopus 로고
    • EasyEnsemble and Feature Selection for Imbalance Data Sets
    • Systems Biology and Intelligent Computing, IJCBS '09, Washington, DC, USA, 3-5 August
    • Liu, T.Y. EasyEnsemble and Feature Selection for Imbalance Data Sets. In Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS '09, Washington, DC, USA, 3-5 August 2009; pp. 517-520
    • (2009) Proceedings of the 2009 International Joint Conference on Bioinformatics , pp. 517-520
    • Liu, T.Y.1
  • 24
    • 85036623598 scopus 로고    scopus 로고
    • Weight-Based Rotation Forest for Hyperspectral Image Classification
    • Feng, W.; Bao, W. Weight-Based Rotation Forest for Hyperspectral Image Classification. IEEE Geosci. Remote Sens. Lett. 2017, 14, 2167-2171
    • (2017) IEEE Geosci. Remote Sens. Lett , vol.14 , pp. 2167-2171
    • Feng, W.1    Bao, W.2
  • 26
    • 84922643075 scopus 로고    scopus 로고
    • Neighbourhood sampling in bagging for imbalanced data
    • Blaszczynski, J.; Stefanowski, J. Neighbourhood sampling in bagging for imbalanced data. Neurocomputing 2015, 150, 529-542
    • (2015) Neurocomputing , vol.150 , pp. 529-542
    • Blaszczynski, J.1    Stefanowski, J.2
  • 27
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods
    • Schapire, R.E.; Freund, Y.; Bartlett, P.; Lee, W.S. Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. Ann. Stat. 1998, 26, 1651-2080
    • (1998) Ann. Stat , vol.26 , pp. 1651-2080
    • Schapire, R.E.1    Freund, Y.2    Bartlett, P.3    Lee, W.S.4
  • 28
    • 84905560678 scopus 로고    scopus 로고
    • Exploiting diversity for optimizing margin distribution in ensemble learning
    • Hu, Q.; Li, L.;Wu, X.; Schaefer, G.; Yu, D. Exploiting diversity for optimizing margin distribution in ensemble learning. Knowl. Based Syst. 2014, 67, 90-104
    • (2014) Knowl. Based Syst , vol.67 , pp. 90-104
    • Hu, Q.1    Li, L.2    Wu, X.3    Schaefer, G.4    Yu, D.5
  • 31
    • 84956598937 scopus 로고    scopus 로고
    • Class noise removal and correction for image classification using ensemble margin
    • Quebec City, QC, Canada, 27-30 September
    • Feng, W.; Boukir, S. Class noise removal and correction for image classification using ensemble margin. In Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 27-30 September 2015; pp. 4698-4702
    • (2015) Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP) , pp. 4698-4702
    • Feng, W.1    Boukir, S.2
  • 32
    • 84962609076 scopus 로고    scopus 로고
    • Identification and correction of mislabeled training data for land cover classification based on ensemble margin
    • Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July
    • Feng, W.; Boukir, S.; Guo, L. Identification and correction of mislabeled training data for land cover classification based on ensemble margin. In Proceedings of the IEEE International, Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July 2015; pp. 4991-4994
    • (2015) Proceedings of the IEEE International , pp. 4991-4994
    • Feng, W.1    Boukir, S.2    Guo, L.3
  • 33
    • 73849133622 scopus 로고    scopus 로고
    • Class Conditional Nearest Neighbor for Large Margin Instance Selection
    • Marchiori, E. Class Conditional Nearest Neighbor for Large Margin Instance Selection. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32, 364-370
    • (2010) IEEE Trans. Pattern Anal. Mach. Intell , vol.32 , pp. 364-370
    • Marchiori, E.1
  • 36
    • 84867873453 scopus 로고    scopus 로고
    • Dynamic classifier ensemble using classification confidence
    • Li, L.J.; Zou, B.; Hu, Q.H.;Wu, X.Q.; Yu, D.R. Dynamic classifier ensemble using classification confidence. Neurocomputing 2013, 99, 581-591
    • (2013) Neurocomputing , vol.99 , pp. 581-591
    • Li, L.J.1    Zou, B.2    Hu, Q.H.3    Wu, X.Q.4    Yu, D.R.5
  • 37
    • 84862796962 scopus 로고    scopus 로고
    • Margin distribution based bagging pruning
    • Xie, Z.X.; Xu, Y.; Hu, Q.H.; Zhu, P.F. Margin distribution based bagging pruning. Neurocomputing 2012, 85, 11-19
    • (2012) Neurocomputing , vol.85 , pp. 11-19
    • Xie, Z.X.1    Xu, Y.2    Hu, Q.H.3    Zhu, P.F.4
  • 39
    • 1442356040 scopus 로고    scopus 로고
    • A Multiple Resampling Method for Learning from Imbalanced Data Sets
    • Estabrooks, A.; Jo, T.; Japkowicz, N. A Multiple Resampling Method for Learning from Imbalanced Data Sets. Comput. Intell. 2004, 20, 18-36
    • (2004) Comput. Intell , vol.20 , pp. 18-36
    • Estabrooks, A.1    Jo, T.2    Japkowicz, N.3
  • 41
    • 27144531570 scopus 로고    scopus 로고
    • A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data
    • Batista, G.E.A.P.A.; Prati, R.C.; Monard, M.C. A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. SIGKDD Explor. Newsl. 2004, 6, 20-29
    • (2004) SIGKDD Explor. Newsl , vol.6 , pp. 20-29
    • Batista, G.E.A.P.A.1    Prati, R.C.2    Monard, M.C.3
  • 42
    • 85037992344 scopus 로고    scopus 로고
    • Addressing the Classification with Imbalanced Data: Open Problems and New Challenges on Class Distribution
    • Wroclaw, Poland, 23-25 May 2011; Corchado, E.; Kurzynski, M., Wozniak, M., Eds.; Springer: Berlin/Heidelberg, Germmany
    • Fernández, A.; García, S.; Herrera, F., Addressing the Classification with Imbalanced Data: Open Problems and New Challenges on Class Distribution. In Hybrid Artificial Intelligent Systems: Proceedings of the HAIS 2011 6th International Conference, Wroclaw, Poland, 23-25 May 2011; Corchado, E.; Kurzynski, M., Wozniak, M., Eds.; Springer: Berlin/Heidelberg, Germmany, 2011; Part I; pp. 1-10
    • (2011) In Hybrid Artificial Intelligent Systems: Proceedings of the HAIS 2011 6th International Conference , pp. 1-10
    • Fernández, A.1    García, S.2    Herrera, F.3
  • 43
    • 34547659409 scopus 로고    scopus 로고
    • KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction
    • Washington, DC, USA, 21 August
    • Zhang, J.; Mani, I. KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction. In Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets, Washington, DC, USA, 21 August 2003
    • (2003) Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets
    • Zhang, J.1    Mani, I.2
  • 44
    • 33947284406 scopus 로고    scopus 로고
    • Boosted Classification Trees and Class Probability/Quantile Estimation
    • Mease, D.; Wyner, A.J.; Buja, A. Boosted Classification Trees and Class Probability/Quantile Estimation. J. Mach. Learn. Res. 2007, 8, 409-439
    • (2007) J. Mach. Learn. Res , vol.8 , pp. 409-439
    • Mease, D.1    Wyner, A.J.2    Buja, A.3
  • 45
    • 84874667219 scopus 로고    scopus 로고
    • Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
    • Fernández, A.; López, V.; Galar, M.; del Jesus, M.J.; Herrera, F. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowl. Based Syst. 2013, 42, 97-110
    • (2013) Knowl. Based Syst , vol.42 , pp. 97-110
    • Fernández, A.1    López, V.2    Galar, M.3    del Jesus, M.J.4    Herrera, F.5
  • 46
    • 84928328671 scopus 로고    scopus 로고
    • Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin
    • Mellor, A.; Boukir, S.; Haywood, A.; Jones, S. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. J. Photogramm. Remote Sens. 2015, 105, 155-168
    • (2015) J. Photogramm. Remote Sens , vol.105 , pp. 155-168
    • Mellor, A.1    Boukir, S.2    Haywood, A.3    Jones, S.4
  • 47
    • 84864153221 scopus 로고    scopus 로고
    • Multiclass Imbalance Problems: Analysis and Potential Solutions
    • Wang, S.; Yao, X. Multiclass Imbalance Problems: Analysis and Potential Solutions. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 2012, 42, 1119-1130
    • (2012) IEEE Trans. Syst. Man Cybern. Part B (Cybern.) , vol.42 , pp. 1119-1130
    • Wang, S.1    Yao, X.2
  • 48
    • 0032355984 scopus 로고    scopus 로고
    • Classification by pairwise coupling
    • Hastie, T.; Batista, G.E. Classification by pairwise coupling. Ann. Stat. 1998, 26, 451-471
    • (1998) Ann. Stat , vol.26 , pp. 451-471
    • Hastie, T.1    Batista, G.E.2
  • 49
    • 56749117943 scopus 로고    scopus 로고
    • In Defense of One-Vs-All Classification
    • Rifkin, R.; Klautau, A. In Defense of One-Vs-All Classification. J. Mach. Learn. Res. 2004, 5, 101-141
    • (2004) J. Mach. Learn. Res , vol.5 , pp. 101-141
    • Rifkin, R.1    Klautau, A.2
  • 51
    • 9444297357 scopus 로고    scopus 로고
    • SMOTEBoost: Improving Prediction of the Minority Class in Boosting
    • Springer: Berlin/Heidelberg
    • Chawla, N.V.; Lazarevic, A.; Hall, L.O.; Bowyer, K.W. SMOTEBoost: Improving Prediction of the Minority Class in Boosting. In Knowledge Discovery in Databases: PKDD 2003; Springer: Berlin/Heidelberg, 2003; Volume 2838; pp. 107-119
    • (2003) Knowledge Discovery in Databases: PKDD 2003 , vol.2838 , pp. 107-119
    • Chawla, N.V.1    Lazarevic, A.2    Hall, L.O.3    Bowyer, K.W.4
  • 52
    • 84878457382 scopus 로고    scopus 로고
    • Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques
    • Thanathamathee, P.; Lursinsap, C. Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques. Pattern Recognit. Lett. 2013, 34, 1339-1347
    • (2013) Pattern Recognit. Lett , vol.34 , pp. 1339-1347
    • Thanathamathee, P.1    Lursinsap, C.2
  • 53
    • 74549206140 scopus 로고    scopus 로고
    • SPSM: A new hybrid data clustering algorithm for nonlinear data analysis
    • Wattanachon, U.; Lursinsap, C. SPSM: A new hybrid data clustering algorithm for nonlinear data analysis. Int. J. Pattern Recognit. Artif. Intell. 2009, 23, 1701-1737
    • (2009) Int. J. Pattern Recognit. Artif. Intell , vol.23 , pp. 1701-1737
    • Wattanachon, U.1    Lursinsap, C.2
  • 54
    • 84964203940 scopus 로고
    • Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy
    • Efron, B.; Tibshirani, R. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Stat. Sci. 1986, 1, 54-75
    • (1986) Stat. Sci , vol.1 , pp. 54-75
    • Efron, B.1    Tibshirani, R.2
  • 58
    • 84941559528 scopus 로고    scopus 로고
    • Diversity Techniques Improve the Performance of the Best Imbalance Learning Ensembles
    • Díez-Pastor, J.F.; Rodríguez, J.J.; García-Osorio, C.I.; Kuncheva, L.I. Diversity Techniques Improve the Performance of the Best Imbalance Learning Ensembles. Inf. Sci. 2015, 325, 98-117
    • (2015) Inf. Sci , vol.325 , pp. 98-117
    • Díez-Pastor, J.F.1    Rodríguez, J.J.2    García-Osorio, C.I.3    Kuncheva, L.I.4
  • 59
    • 84881072864 scopus 로고    scopus 로고
    • EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
    • Galar, M.; Fernández, A.; Barrenechea, E.; Herrera, F. EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognit. 2013, 46, 3460-3471
    • (2013) Pattern Recognit , vol.46 , pp. 3460-3471
    • Galar, M.1    Fernández, A.2    Barrenechea, E.3    Herrera, F.4
  • 61
    • 34247205823 scopus 로고    scopus 로고
    • Multi-Class Learning by Smoothed Boosting
    • Jin, R.; Zhang, J. Multi-Class Learning by Smoothed Boosting. Mach. Learn. 2007, 67, 207-227
    • (2007) Mach. Learn , vol.67 , pp. 207-227
    • Jin, R.1    Zhang, J.2
  • 64
    • 85029881657 scopus 로고    scopus 로고
    • Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification
    • Nejatian, S.; Parvin, H.; Faraji, E. Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification. Neurocomputing 2018, 276, 55-66
    • (2018) Neurocomputing , vol.276 , pp. 55-66
    • Nejatian, S.1    Parvin, H.2    Faraji, E.3
  • 65
  • 66
    • 84926525100 scopus 로고    scopus 로고
    • Coupling different methods for overcoming the class imbalance problem
    • Nanni, L.; Fantozzi, C.; Lazzarini, N. Coupling different methods for overcoming the class imbalance problem. Neurocomputing 2015, 158, 48-61
    • (2015) Neurocomputing , vol.158 , pp. 48-61
    • Nanni, L.1    Fantozzi, C.2    Lazzarini, N.3
  • 67
    • 84890363076 scopus 로고    scopus 로고
    • Ensembles of (alpha)-Trees for Imbalanced Classification Problems
    • Park, Y.; Ghosh, J. Ensembles of (alpha)-Trees for Imbalanced Classification Problems. IEEE Trans. Knowl. Data Eng. 2014, 26, 131-143
    • (2014) IEEE Trans. Knowl. Data Eng , vol.26 , pp. 131-143
    • Park, Y.1    Ghosh, J.2
  • 69
  • 70
    • 84873737900 scopus 로고    scopus 로고
    • Margin-based ordered aggregation for ensemble pruning
    • Guo, L.; Boukir, S. Margin-based ordered aggregation for ensemble pruning. Pattern Recognit. Lett. 2013, 34, 603-609
    • (2013) Pattern Recognit. Lett , vol.34 , pp. 603-609
    • Guo, L.1    Boukir, S.2
  • 71
    • 0003619255 scopus 로고    scopus 로고
    • Technical Report 460; Statistics Department, University of California at Berkeley: Berkeley, CA, USA
    • Breiman, L. Bias, Variance, and Arcing Classifiers; Technical Report 460; Statistics Department, University of California at Berkeley: Berkeley, CA, USA, 1996
    • (1996) Bias, Variance, and Arcing Classifiers
    • Breiman, L.1
  • 73
    • 0002872346 scopus 로고    scopus 로고
    • Bias Plus Variance Decomposition for Zero-One Loss Functions
    • Morgan Kaufmann Publishers: San Mateo, CA, USA
    • Kohavi, R.; Wolpert, D.H. Bias Plus Variance Decomposition for Zero-One Loss Functions. In Proceedings of the Thirteenth International on Machine Learning; Morgan Kaufmann Publishers: San Mateo, CA, USA, 1996; pp. 275-283
    • (1996) Proceedings of the Thirteenth International on Machine Learning , pp. 275-283
    • Kohavi, R.1    Wolpert, D.H.2
  • 74
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demsar, J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 2006, 7, 1-30
    • (2006) J. Mach. Learn. Res , vol.7 , pp. 1-30
    • Demsar, J.1
  • 75
    • 58149287952 scopus 로고    scopus 로고
    • An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons
    • Garcia, S.; Herrera, F. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons. J. Mach. Learn. Res. 2008, 9, 2677-2694
    • (2008) J. Mach. Learn. Res , vol.9 , pp. 2677-2694
    • Garcia, S.1    Herrera, F.2
  • 77
    • 84861810464 scopus 로고    scopus 로고
    • Inverse random under sampling for class imbalance problem and its application to multi-label classification
    • Tahir, M.A.; Kittler, J.; Yan, F. Inverse random under sampling for class imbalance problem and its application to multi-label classification. Pattern Recognit. 2012, 45, 3738-3750
    • (2012) Pattern Recognit , vol.45 , pp. 3738-3750
    • Tahir, M.A.1    Kittler, J.2    Yan, F.3
  • 78
    • 0037403516 scopus 로고    scopus 로고
    • Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
    • Kuncheva, L.I.; Whitaker, C.J. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Mach. Learn. 2003, 51, 181-207
    • (2003) Mach. Learn , vol.51 , pp. 181-207
    • Kuncheva, L.I.1    Whitaker, C.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.