{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T04:07:31Z","timestamp":1751083651354,"version":"3.41.0"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319721491"},{"type":"electronic","value":"9783319721507"}],"license":[{"start":{"date-parts":[[2017,11,27]],"date-time":"2017-11-27T00:00:00Z","timestamp":1511740800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-72150-7_13","type":"book-chapter","created":{"date-parts":[[2017,11,26]],"date-time":"2017-11-26T13:21:29Z","timestamp":1511702489000},"page":"153-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Community-Based Feature Selection for Credit Card Default Prediction"],"prefix":"10.1007","author":[{"given":"Qiucheng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yanmei","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,11,27]]},"reference":[{"issue":"7","key":"13_CR1","first-page":"36","volume":"145","author":"A Ajay","year":"2016","unstructured":"Ajay, A., Venkatesh, A., Gracia, S., et al.: Prediction of credit-card defaulters: a comparative study on performance of classifiers. Int. J. Comput. Appl. 145(7), 36\u201341 (2016)","journal-title":"Int. J. Comput. Appl."},{"issue":"2","key":"13_CR2","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.ejor.2015.10.001","volume":"249","author":"M Leow","year":"2016","unstructured":"Leow, M., Crook, J.: A new Mixture model for the estimation of credit card Exposure at Default. Eur. J. Oper. Res. 249(2), 487\u2013497 (2016)","journal-title":"Eur. J. Oper. Res."},{"key":"13_CR3","unstructured":"Sun, S.H., Jin, Z.: Estimating credit risk parameters using ensemble learning methods: an empirical study on loss given default. J. Credit Risk (August 9, 2016). (Forthcoming)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Bermingham, M.L., Pongwong, R., Spiliopoulou, A., et al. Application of high-dimensional feature selection: evaluation for genomic prediction in man. Sci. Rep. 10312\u201310312 (2015)","DOI":"10.1038\/srep10312"},{"key":"13_CR5","unstructured":"Li J., Cheng K., Wang S., et al.: Feature Selection: A Data Perspective (2016). arXiv:1601.07996"},{"issue":"3","key":"13_CR6","first-page":"110","volume":"21","author":"S Alelyani","year":"2016","unstructured":"Alelyani, S., Tang, J., Liu, H.: Feature selection for clustering: a review. Encycl. Database Syst. 21(3), 110\u2013121 (2016)","journal-title":"Encycl. Database Syst."},{"issue":"1","key":"13_CR7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.knosys.2016.03.023","volume":"103","author":"HA Abdou","year":"2016","unstructured":"Abdou, H.A., Tsafack, M., Ntim, C.G., et al.: Predicting creditworthiness in retail banking with limited scoring data. Knowl Based Syst 103(1), 89\u2013103 (2016)","journal-title":"Knowl Based Syst"},{"issue":"2","key":"13_CR8","doi-asserted-by":"crossref","first-page":"e0117844","DOI":"10.1371\/journal.pone.0117844","volume":"10","author":"H Wang","year":"2015","unstructured":"Wang, H., Xu, Q., Zhou, L., et al.: Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble. PLOS ONE 10(2), e0117844 (2015)","journal-title":"PLOS ONE"},{"issue":"2","key":"13_CR9","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.ejor.2014.12.014","volume":"249","author":"PS Hon","year":"2016","unstructured":"Hon, P.S., Bellotti, T.: Models and forecasts of credit card balance. Eur. J. Oper. Res. 249(2), 498\u2013505 (2016)","journal-title":"Eur. J. Oper. Res."},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Evangelista, R.D., Artes R.: Using multi-state markov models to identify credit card risk. Production 26(2), The Scientific Electronic Library Online (2016)","DOI":"10.1590\/0103-6513.160814"},{"issue":"2","key":"13_CR11","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1145\/2594454","volume":"5","author":"J Yang","year":"2014","unstructured":"Yang, J., Leskovec, J.: Structure and overlaps of ground-truth communities in networks. ACM Trans. Intell. Syst. Technol. 5(2), 26 (2014)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"13_CR12","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ins.2016.03.028","volume":"355\u2013356","author":"Y Hu","year":"2016","unstructured":"Hu, Y., Yang, B., Wong, H.: A weighted local view method based on observation over ground truth for community detection. Inf. Sci. 355\u2013356, 37\u201357 (2016)","journal-title":"Inf. Sci."},{"issue":"8","key":"13_CR13","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1007\/s00521-017-3033-5","volume":"28","author":"Y Hu","year":"2017","unstructured":"Hu, Y., Yang, B.: Characterizing the structure of large real networks to improve community detection. Neural Comput. Appl. 28(8), 2363\u20132363 (2017)","journal-title":"Neural Comput. Appl."},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Nie, G., Wang, G., Zhang, P., et al.: Finding the hidden pattern of credit card holder\u2019s churn: a case of China. In: International Conference on Computational Science (ICCS 2009), pp. 561\u2013569. Springer, Berlin, Heidelberg (2009)","DOI":"10.1007\/978-3-642-01973-9_63"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, B., Wang, W., Xue, G., et al.: An empirical analysis on temporal pattern of credit card trade. In: International Conference in Swarm Intelligence, pp. 63\u201370. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-20472-7_7"},{"issue":"1","key":"13_CR16","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1145\/2877204","volume":"41","author":"C Zhang","year":"2016","unstructured":"Zhang, C., Kumar, A., R\u00e9, C.: Materialization optimizations for feature selection workloads. ACM Trans. Database Syst. 41(1), 2 (2016)","journal-title":"ACM Trans. Database Syst."},{"key":"13_CR17","volume-title":"Machine Learning in Bioinformatics","author":"SY Kung","year":"2009","unstructured":"Kung, S.Y., Mak, M.W.: Chapter 1: Feature Selection for genomic and proteomic datamining. Machine Learning in Bioinformatics. Wiley, Hoboken, NJ, USA (2009)"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Boln-Canedo, V., Snchez-Maroo, N., Alonso-Betanzos, A.: Feature selection for high-dimensional data. Springer International Publishing (2015)","DOI":"10.1007\/978-3-319-21858-8"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Asir Antony Gnana Singh, D., Appavu alias Balamurugan, S., Jebamalar Leavline, E.: Literature review on feature selection methods for high-dimensional data. Methods 136(1) (2016)","DOI":"10.5120\/ijca2016908317"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L:. Feature Extraction: Foundations and Applications. Springer (2006)","DOI":"10.1007\/978-3-540-35488-8"},{"issue":"3","key":"13_CR21","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1080\/09540091.2016.1149146","volume":"28","author":"AJ Tall\u00f3n-Ballesteros","year":"2016","unstructured":"Tall\u00f3n-Ballesteros, A.J., Riquelme, J.C., Ruiz, R.: Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks. Connect. Sci. 28(3), 242\u2013257 (2016)","journal-title":"Connect. Sci."},{"key":"13_CR22","unstructured":"Peng, H., Ding, C., Long, F.: Minimum redundancy-maximum relevance feature selection and its applications. Feature Selection (2015)"},{"issue":"12","key":"13_CR23","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1016\/j.patcog.2005.11.001","volume":"39","author":"R Ruiz","year":"2006","unstructured":"Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recognit. 39(12), 2383\u20132392 (2006)","journal-title":"Pattern Recognit."},{"key":"13_CR24","first-page":"1","volume":"99","author":"L Ma","year":"2017","unstructured":"Ma, L., Li, M., Gao, Y., et al.: A novel wrapper approach for feature selection in object-based image classification using polygon-based cross-validation. IEEE Geosci. Remote Sens. Lett. 99, 1\u20135 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"13_CR25","unstructured":"Mej\u00eda-Lavalle, M., Sucar, E., Arroyo, G.: Feature selection with a perceptron neural networks. In: International Workshop on Feature Selection for Data Mining, pp. 131\u2013135 (2006)"},{"issue":"23","key":"13_CR26","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jchromb.2012.05.020","volume":"910","author":"X Lin","year":"2012","unstructured":"Lin, X., Yang, F., Zhou, L., et al.: A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 910(23), 149\u2013155 (2012)","journal-title":"J. Chromatogr. B Anal. Technol. Biomed. Life Sci."},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Fu, H., Xiao, Z., Dellandr\u00e9a, E., et al.: Image categorization using ESFS: a new embedded feature selection method based on SFS. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 288\u2013299. Bordeaux, France (2009)","DOI":"10.1007\/978-3-642-04697-1_27"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Butterworth, R., Piatetskyshapiro, G., Simovici, D.A., et al.: On feature selection through clustering. In: 5th International Conference on Data Mining, pp. 581\u2013584 (2005)","DOI":"10.1109\/ICDM.2005.106"},{"key":"13_CR29","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.procs.2014.05.283","volume":"31","author":"X Zhou","year":"2014","unstructured":"Zhou, X., Hu, Y., Guo, L., et al.: Text categorization based on clustering feature selection. Procedia Comput. Sci. 31, 398\u2013405 (2014)","journal-title":"Procedia Comput. Sci."},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Han, D., Kim, J.: Unsupervised simultaneous orthogonal basis clustering feature selection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5016\u20135023 (2015)","DOI":"10.1109\/CVPR.2015.7299136"},{"key":"13_CR31","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1098\/rspl.1895.0041","volume":"58","author":"K Pearson","year":"1895","unstructured":"Pearson, K.: Note on regression and inheritance in the case of two parents. Proc. R. Soc. Lond. 58, 240\u2013242 (1895)","journal-title":"Proc. R. Soc. Lond."},{"issue":"11","key":"13_CR32","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.ecns.2014.07.010","volume":"10","author":"S Prion","year":"2014","unstructured":"Prion, S., Haerling, K.A.: Making sense of methods and measurement: pearson product-moment correlation coefficient. Clin. Simul. Nurs. 10(11), 587\u2013588 (2014)","journal-title":"Clin. Simul. Nurs."},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Sedgwick, P.: Spearman\u2019s rank correlation coefficient. BMJ (2014)","DOI":"10.1136\/bmj.g7327"},{"key":"13_CR34","unstructured":"Kim, T., Wright, S.: PMU placement for line outage identification via multinomial logistic regression. IEEE Trans. Smart Grid (2016)"},{"issue":"3","key":"13_CR35","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1023\/A:1007614523901","volume":"37","author":"RE Schapire","year":"1999","unstructured":"Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297\u2013336 (1999)","journal-title":"Mach. Learn."},{"key":"13_CR36","unstructured":"Wang, W., Lin, W., Zhang, R., et al.: Research on human face location based on Adaboost and convolutional neural network. In: IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (2017)"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Byun, H., Lee, S.: Applications of support vector machines for pattern recognition: a survey. Lecture Notes in Computer Science (2002)","DOI":"10.1007\/3-540-45665-1_17"},{"key":"13_CR38","unstructured":"Deng, H., Runger, G.: Feature selection via regularized trees. Int. Jt. Conf. Neural Netw. (2015)"},{"key":"13_CR39","unstructured":"Liu, H., Motoda, H., Yu, L.: A Selective Sampling Approach to Active Feature Selection. Elsevier Science Publishers Ltd. (2015)"},{"issue":"3","key":"13_CR40","first-page":"754","volume":"9","author":"A Sharma","year":"2015","unstructured":"Sharma, A., Imoto, S., Miyano, S.: A Top-r feature selection algorithm for microarray gene expression data. IEEE\/ACM Trans. Comput. Biol. Bioinform. 9(3), 754\u2013764 (2015)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Arunkumar, C., Ramakrishnan, S.: A hybrid approach to feature selection using correlation coefficient and fuzzy rough quick reduct algorithm applied to cancer microarray data. Int. Conf. Intell. Syst. Control (2016)","DOI":"10.1109\/ISCO.2016.7726921"},{"issue":"8","key":"13_CR42","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves. In: 23rd International Conference on Machine Learning, pp. 233\u2013240. ACM, Pennsylvania, USA (2006)","DOI":"10.1145\/1143844.1143874"}],"container-title":["Studies in Computational Intelligence","Complex Networks &amp; Their Applications VI"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-72150-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T14:24:40Z","timestamp":1751034280000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-72150-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,27]]},"ISBN":["9783319721491","9783319721507"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-72150-7_13","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2017,11,27]]}}}