메뉴 건너뛰기




Volumn 78, Issue , 2017, Pages 225-241

A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring

Author keywords

Bayesian hyper parameter optimization; Boosted decision tree; Credit scoring

Indexed keywords

DATA HANDLING; DECISION TREES;

EID: 85013170820     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2017.02.017     Document Type: Article
Times cited : (607)

References (67)
  • 1
    • 84992292647 scopus 로고    scopus 로고
    • Classifiers consensus system approach for credit scoring
    • Ala'raj, M., Abbod, M.F., Classifiers consensus system approach for credit scoring. Knowledge-Based Systems 104 (2016), 89–105.
    • (2016) Knowledge-Based Systems , vol.104 , pp. 89-105
    • Ala'raj, M.1    Abbod, M.F.2
  • 2
    • 84979529983 scopus 로고    scopus 로고
    • A new hybrid ensemble credit scoring model based on classifiers consensus system approach
    • Ala'raj, M., Abbod, M.F., A new hybrid ensemble credit scoring model based on classifiers consensus system approach. Expert Systems with Applications 64 (2016), 36–55.
    • (2016) Expert Systems with Applications , vol.64 , pp. 36-55
    • Ala'raj, M.1    Abbod, M.F.2
  • 3
    • 84980104458 scopus 로고
    • Financial ratios, discriminant analysis and the prediction of corporate bankruptcy
    • Altman, E.I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23 (1968), 589–609.
    • (1968) The Journal of Finance , vol.23 , pp. 589-609
    • Altman, E.I.1
  • 5
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Bauer, E., Kohavi, R., An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36 (1999), 105–139.
    • (1999) Machine Learning , vol.36 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 9
    • 33846516584 scopus 로고    scopus 로고
    • Pattern recognition and machine learning
    • Springer New York
    • Bishop, C.M., Pattern recognition and machine learning. 2006, Springer, New York.
    • (2006)
    • Bishop, C.M.1
  • 10
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L., Bagging predictors. Machine Learning 24 (1996), 123–140.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 11
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L., Random forests. Machine Learning 45 (2001), 5–32.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 13
    • 84939787273 scopus 로고    scopus 로고
    • Investigating the use of gradient boosting machine, random forest and their ensemble to predict skin flavonoid content from berry physical–mechanical characteristics in wine grapes
    • Brillante, L., Gaiotti, F., Lovat, L., Vincenzi, S., Giacosa, S., Torchio, F., et al. Investigating the use of gradient boosting machine, random forest and their ensemble to predict skin flavonoid content from berry physical–mechanical characteristics in wine grapes. Computers and Electronics in Agriculture 117 (2015), 186–193.
    • (2015) Computers and Electronics in Agriculture , vol.117 , pp. 186-193
    • Brillante, L.1    Gaiotti, F.2    Lovat, L.3    Vincenzi, S.4    Giacosa, S.5    Torchio, F.6
  • 14
    • 80255133264 scopus 로고    scopus 로고
    • An experimental comparison of classification algorithms for imbalanced credit scoring data sets
    • Brown, I., Mues, C., An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications 39 (2012), 3446–3453.
    • (2012) Expert Systems with Applications , vol.39 , pp. 3446-3453
    • Brown, I.1    Mues, C.2
  • 16
    • 84925537862 scopus 로고    scopus 로고
    • Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution
    • Chen, S., Montgomery, J., Bolufé-Röhler, A., Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution. Applied Intelligence 42 (2015), 514–526.
    • (2015) Applied Intelligence , vol.42 , pp. 514-526
    • Chen, S.1    Montgomery, J.2    Bolufé-Röhler, A.3
  • 17
    • 85013165026 scopus 로고    scopus 로고
    • Xgboost: A scalable tree boosting system. arXiv preprint arXiv:
    • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. arXiv preprint arXiv: 1603.02754.
    • (2016)
    • Chen, T.1    Guestrin, C.2
  • 18
    • 85013156587 scopus 로고    scopus 로고
    • (2015). xgboost: EXtreme Gradient Boosting. R package version 0.4-2.
    • Chen, T., & He, T. (2015). xgboost: EXtreme Gradient Boosting. R package version 0.4-2.
    • Chen, T.1    He, T.2
  • 19
    • 84958547251 scopus 로고    scopus 로고
    • Group social capital and lending outcomes in the financial credit market: An empirical study of online peer-to-peer lending
    • Chen, X., Zhou, L., Wan, D., Group social capital and lending outcomes in the financial credit market: An empirical study of online peer-to-peer lending. Electronic Commerce Research and Applications 15 (2016), 1–13.
    • (2016) Electronic Commerce Research and Applications , vol.15 , pp. 1-13
    • Chen, X.1    Zhou, L.2    Wan, D.3
  • 20
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes, C., Vapnik, V., Support-vector networks. Machine Learning 20 (1995), 273–297.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 21
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar, J., Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006), 1–30.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 22
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar, J., Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7 (2006), 1–30.
    • (2006) The Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 23
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • Dietterich, T.G., An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine learning 40 (2000), 139–157.
    • (2000) Machine learning , vol.40 , pp. 139-157
    • Dietterich, T.G.1
  • 26
    • 78650417769 scopus 로고    scopus 로고
    • Multiple classifier architectures and their application to credit risk assessment
    • Finlay, S., Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research 210 (2011), 368–378.
    • (2011) European Journal of Operational Research , vol.210 , pp. 368-378
    • Finlay, S.1
  • 27
    • 84926638352 scopus 로고    scopus 로고
    • Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal
    • Florez-Lopez, R., Ramon-Jeronimo, J.M., Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal. Expert Systems with Applications 42 (2015), 5737–5753.
    • (2015) Expert Systems with Applications , vol.42 , pp. 5737-5753
    • Florez-Lopez, R.1    Ramon-Jeronimo, J.M.2
  • 29
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: A gradient boosting machine
    • Friedman, J.H., Greedy function approximation: A gradient boosting machine. Annals of statistics 29 (2001), 1189–1232.
    • (2001) Annals of statistics , vol.29 , pp. 1189-1232
    • Friedman, J.H.1
  • 31
    • 80255138189 scopus 로고    scopus 로고
    • Gradient boosting trees for auto insurance loss cost modeling and prediction
    • Guelman, L., Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications 39 (2012), 3659–3667.
    • (2012) Expert Systems with Applications , vol.39 , pp. 3659-3667
    • Guelman, L.1
  • 32
    • 84952978996 scopus 로고    scopus 로고
    • Instance-based credit risk assessment for investment decisions in P2P lending
    • Guo, Y., Zhou, W., Luo, C., Liu, C., Xiong, H., Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research 249 (2016), 417–426.
    • (2016) European Journal of Operational Research , vol.249 , pp. 417-426
    • Guo, Y.1    Zhou, W.2    Luo, C.3    Liu, C.4    Xiong, H.5
  • 33
    • 33745886270 scopus 로고    scopus 로고
    • Classifier technology and the illusion of progress
    • Hand, D.J., Classifier technology and the illusion of progress. Statistical science 21 (2006), 1–14.
    • (2006) Statistical science , vol.21 , pp. 1-14
    • Hand, D.J.1
  • 34
    • 69549133517 scopus 로고    scopus 로고
    • Measuring classifier performance: A coherent alternative to the area under the ROC curve
    • Hand, D.J., Measuring classifier performance: A coherent alternative to the area under the ROC curve. Machine learning 77 (2009), 103–123.
    • (2009) Machine learning , vol.77 , pp. 103-123
    • Hand, D.J.1
  • 36
    • 84907481786 scopus 로고    scopus 로고
    • Credit scoring using the clustered support vector machine
    • Harris, T., Credit scoring using the clustered support vector machine. Expert Systems with Applications 42 (2015), 741–750.
    • (2015) Expert Systems with Applications , vol.42 , pp. 741-750
    • Harris, T.1
  • 38
    • 34047126555 scopus 로고    scopus 로고
    • Credit scoring with a data mining approach based on support vector machines
    • Huang, C.-L., Chen, M.-C., Wang, C.-J., Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33 (2007), 847–856.
    • (2007) Expert Systems with Applications , vol.33 , pp. 847-856
    • Huang, C.-L.1    Chen, M.-C.2    Wang, C.-J.3
  • 41
    • 26444554549 scopus 로고    scopus 로고
    • Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
    • Lee, T.-S., Chiu, C.-C., Chou, Y.-C., Lu, C.-J., Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis 50 (2006), 1113–1130.
    • (2006) Computational Statistics & Data Analysis , vol.50 , pp. 1113-1130
    • Lee, T.-S.1    Chiu, C.-C.2    Chou, Y.-C.3    Lu, C.-J.4
  • 42
    • 84937523151 scopus 로고    scopus 로고
    • Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
    • Lessmann, S., Baesens, B., Seow, H.-V., Thomas, L.C., Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research 247 (2015), 124–136.
    • (2015) European Journal of Operational Research , vol.247 , pp. 124-136
    • Lessmann, S.1    Baesens, B.2    Seow, H.-V.3    Thomas, L.C.4
  • 43
    • 48749109333 scopus 로고    scopus 로고
    • Particle swarm optimization for parameter determination and feature selection of support vector machines
    • Lin, S.-W., Ying, K.-C., Chen, S.-C., Lee, Z.-J., Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications 35 (2008), 1817–1824.
    • (2008) Expert Systems with Applications , vol.35 , pp. 1817-1824
    • Lin, S.-W.1    Ying, K.-C.2    Chen, S.-C.3    Lee, Z.-J.4
  • 44
    • 17844363481 scopus 로고    scopus 로고
    • Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
    • Min, J.H., Lee, Y.-C., Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 28 (2005), 603–614.
    • (2005) Expert Systems with Applications , vol.28 , pp. 603-614
    • Min, J.H.1    Lee, Y.-C.2
  • 45
    • 56349112745 scopus 로고    scopus 로고
    • An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
    • Nanni, L., Lumini, A., An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications 36 (2009), 3028–3033.
    • (2009) Expert Systems with Applications , vol.36 , pp. 3028-3033
    • Nanni, L.1    Lumini, A.2
  • 46
    • 84899957756 scopus 로고    scopus 로고
    • Integrating complementary techniques for promoting diversity in classifier ensembles: A systematic study
    • Nascimento, D.S., Coelho, A.L., Canuto, A.M., Integrating complementary techniques for promoting diversity in classifier ensembles: A systematic study. Neurocomputing 138 (2014), 347–357.
    • (2014) Neurocomputing , vol.138 , pp. 347-357
    • Nascimento, D.S.1    Coelho, A.L.2    Canuto, A.M.3
  • 47
    • 80052022630 scopus 로고    scopus 로고
    • Credit card churn forecasting by logistic regression and decision tree
    • Nie, G., Rowe, W., Zhang, L., Tian, Y., Shi, Y., Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications 38 (2011), 15273–15285.
    • (2011) Expert Systems with Applications , vol.38 , pp. 15273-15285
    • Nie, G.1    Rowe, W.2    Zhang, L.3    Tian, Y.4    Shi, Y.5
  • 51
    • 84977612604 scopus 로고    scopus 로고
    • The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending
    • Serrano-Cinca, C., Gutiérrez-Nieto, B., The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decision Support Systems 89 (2016), 113–122.
    • (2016) Decision Support Systems , vol.89 , pp. 113-122
    • Serrano-Cinca, C.1    Gutiérrez-Nieto, B.2
  • 52
    • 24944547920 scopus 로고    scopus 로고
    • Neural networks: A comprehensive foundation
    • Prentice-Hall of India New Delhi
    • Simon, H., Neural networks: A comprehensive foundation. 2008, Prentice-Hall of India, New Delhi.
    • (2008)
    • Simon, H.1
  • 54
    • 38349046524 scopus 로고    scopus 로고
    • 11-classification and regression trees, bagging, and boosting
    • Sutton, C.D., 11-classification and regression trees, bagging, and boosting. Handbook of Statistics 24 (2005), 303–329.
    • (2005) Handbook of Statistics , vol.24 , pp. 303-329
    • Sutton, C.D.1
  • 56
    • 84908455123 scopus 로고    scopus 로고
    • A comparative study of classifier ensembles for bankruptcy prediction
    • Tsai, C.-F., Hsu, Y.-F., Yen, D.C., A comparative study of classifier ensembles for bankruptcy prediction. Applied Soft Computing 24 (2014), 977–984.
    • (2014) Applied Soft Computing , vol.24 , pp. 977-984
    • Tsai, C.-F.1    Hsu, Y.-F.2    Yen, D.C.3
  • 57
    • 71349085322 scopus 로고    scopus 로고
    • Multiple classifier application to credit risk assessment
    • Twala, B., Multiple classifier application to credit risk assessment. Expert Systems with Applications 37 (2010), 3326–3336.
    • (2010) Expert Systems with Applications , vol.37 , pp. 3326-3336
    • Twala, B.1
  • 58
    • 77956619458 scopus 로고    scopus 로고
    • A comparative assessment of ensemble learning for credit scoring
    • Wang, G., Hao, J., Ma, J., Jiang, H., A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications 38 (2011), 223–230.
    • (2011) Expert Systems with Applications , vol.38 , pp. 223-230
    • Wang, G.1    Hao, J.2    Ma, J.3    Jiang, H.4
  • 59
    • 84155181098 scopus 로고    scopus 로고
    • Two credit scoring models based on dual strategy ensemble trees
    • Wang, G., Ma, J., Huang, L., Xu, K., Two credit scoring models based on dual strategy ensemble trees. Knowledge-Based Systems 26 (2012), 61–68.
    • (2012) Knowledge-Based Systems , vol.26 , pp. 61-68
    • Wang, G.1    Ma, J.2    Huang, L.3    Xu, K.4
  • 60
    • 0034118581 scopus 로고    scopus 로고
    • Neural network credit scoring models
    • West, D., Neural network credit scoring models. Computers & operations research 27 (2000), 1131–1152.
    • (2000) Computers & operations research , vol.27 , pp. 1131-1152
    • West, D.1
  • 61
    • 84974012809 scopus 로고
    • A note on the comparison of logit and discriminant models of consumer credit behavior
    • Wiginton, J.C., A note on the comparison of logit and discriminant models of consumer credit behavior. Journal of Financial and Quantitative Analysis 15 (1980), 757–770.
    • (1980) Journal of Financial and Quantitative Analysis , vol.15 , pp. 757-770
    • Wiginton, J.C.1
  • 63
    • 84866478887 scopus 로고    scopus 로고
    • Credit risk assessment and decision making by a fusion approach
    • Wu, T.-C., Hsu, M.-F., Credit risk assessment and decision making by a fusion approach. Knowledge-Based Systems 35 (2012), 102–110.
    • (2012) Knowledge-Based Systems , vol.35 , pp. 102-110
    • Wu, T.-C.1    Hsu, M.-F.2
  • 64
    • 84861589689 scopus 로고    scopus 로고
    • A hybrid KMV model, random forests and rough set theory approach for credit rating
    • Yeh, C.-C., Lin, F., Hsu, C.-Y., A hybrid KMV model, random forests and rough set theory approach for credit rating. Knowledge-Based Systems 33 (2012), 166–172.
    • (2012) Knowledge-Based Systems , vol.33 , pp. 166-172
    • Yeh, C.-C.1    Lin, F.2    Hsu, C.-Y.3
  • 66
    • 84985036688 scopus 로고    scopus 로고
    • Research on Credit Scoring by Fusing Social Media Information in Online Peer-to-Peer Lending
    • Zhang, Y., Jia, H., Diao, Y., Hai, M., Li, H., Research on Credit Scoring by Fusing Social Media Information in Online Peer-to-Peer Lending. Procedia Computer Science 91 (2016), 168–174.
    • (2016) Procedia Computer Science , vol.91 , pp. 168-174
    • Zhang, Y.1    Jia, H.2    Diao, Y.3    Hai, M.4    Li, H.5
  • 67
    • 84963595084 scopus 로고    scopus 로고
    • Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction
    • Zięba, M., Tomczak, S.K., Tomczak, J.M., Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications 58 (2016), 93–101.
    • (2016) Expert Systems with Applications , vol.58 , pp. 93-101
    • Zięba, M.1    Tomczak, S.K.2    Tomczak, J.M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.