{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:06:18Z","timestamp":1742929578395,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319591469"},{"type":"electronic","value":"9783319591476"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-59147-6_46","type":"book-chapter","created":{"date-parts":[[2017,5,16]],"date-time":"2017-05-16T21:04:08Z","timestamp":1494968648000},"page":"538-548","source":"Crossref","is-referenced-by-count":8,"title":["Combining Ranking with Traditional Methods for Ordinal Class Imbalance"],"prefix":"10.1007","author":[{"given":"Ricardo","family":"Cruz","sequence":"first","affiliation":[]},{"given":"Kelwin","family":"Fernandes","sequence":"additional","affiliation":[]},{"given":"Joaquim F.","family":"Pinto Costa","sequence":"additional","affiliation":[]},{"given":"Mar\u00eda","family":"P\u00e9rez\u00a0Ortiz","sequence":"additional","affiliation":[]},{"given":"Jaime S.","family":"Cardoso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,5,18]]},"reference":[{"key":"46_CR1","doi-asserted-by":"crossref","unstructured":"Cruz, R., Fernandes, K., Cardoso, J.S., Pinto Costa, J.F.: Tackling class imbalance with ranking. In: International Joint Conference on Neural Networks (IJCNN). IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727469"},{"key":"46_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1007\/978-3-319-59147-6_46","volume-title":"IWANN 2017, Part II","author":"R Cruz","year":"2017","unstructured":"Cruz, R., Fernandes, K., Pinto Costa, J.F., Perez Ortiz, M., Cardoso, J.S.: Ordinal class imbalance with ranking. In: Rojas, I., et al. (eds.) IWANN 2017, Part II. LNCS, vol. 10306, pp. 538\u2013548. Springer, Cham (2017)"},{"issue":"Jul","key":"46_CR3","first-page":"1393","volume":"8","author":"JS Cardoso","year":"2007","unstructured":"Cardoso, J.S., Costa, J.F.: Learning to classify ordinal data: the data replication method. J. Mach. Learn. Res. 8(Jul), 1393\u20131429 (2007)","journal-title":"J. Mach. Learn. Res."},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Chu, W., Sathiya Keerthi, S.: New approaches to support vector ordinal regression. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 145\u2013152. ACM (2005)","DOI":"10.1145\/1102351.1102370"},{"key":"46_CR5","doi-asserted-by":"crossref","unstructured":"Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: Ninth International Conference on Artificial Neural Networks, ICANN 1999, (Conf. Publ. No. 470), vol. 1, pp. 97\u2013102. IET (1999)","DOI":"10.1049\/cp:19991091"},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Pinto Costa, J.F., Sousa, R., Cardoso, J.S.: An all-at-once unimodal SVM approach for ordinal classification. In: Ninth International Conference on Machine Learning and Applications (ICMLA), pp. 59\u201364. IEEE (2010)","DOI":"10.1109\/ICMLA.2010.16"},{"key":"46_CR7","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"5","key":"46_CR8","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1109\/TKDE.2014.2365780","volume":"27","author":"M P\u00e9rez-Ortiz","year":"2015","unstructured":"P\u00e9rez-Ortiz, M., Guti\u00e9rrez, P.A., Herv\u00e1s-Mart\u00ednez, C., Yao, X.: Graph-based approaches for over-sampling in the context of ordinal regression. IEEE Trans. Knowl. Data Eng. 27(5), 1233\u20131245 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"46_CR9","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","volume":"39","author":"X-Y Liu","year":"2009","unstructured":"Liu, X.-Y., Jianxin, W., Zhou, Z.-H.: Exploratory undersampling for class imbalance learning. IEEE Trans. Syst. Man Cybern. 39(2), 539\u2013550 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"5","key":"46_CR10","first-page":"160","volume":"2","author":"M Sahare","year":"2012","unstructured":"Sahare, M., Gupta, H.: A review of multi-class classification for imbalanced data. Int. J. Adv. Comput. Res. 2(5), 160\u2013164 (2012)","journal-title":"Int. J. Adv. Comput. Res."},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Hu, S., Liang, Y., Ma, L., He, Y.: MSMOTE: improving classification performance when training data is imbalanced. In: 2nd International Workshop on Computer Science and Engineering, WCSE 2009, vol. 2, pp. 13\u201317 (2009)","DOI":"10.1109\/WCSE.2009.756"},{"key":"46_CR12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.neucom.2013.05.058","volume":"135","author":"M Cruz-Ram\u00edrez","year":"2014","unstructured":"Cruz-Ram\u00edrez, M., Herv\u00e1s-Mart\u00ednez, C., S\u00e1nchez-Monedero, J., Guti\u00e9rrez, P.A.: Metrics to guide a multi-objective evolutionary algorithm for ordinal classification. Neurocomputing 135, 21\u201331 (2014)","journal-title":"Neurocomputing"},{"key":"46_CR13","unstructured":"Lichman, M.: UCI Machine Learning Repository (2013). http:\/\/archive.ics.uci.edu\/ml"},{"key":"46_CR14","unstructured":"PASCAL. Pascal (pattern analysis, statistical modelling and computational learning) machine learning benchmarks repository (2011). http:\/\/mldata.org\/"},{"issue":"Jul","key":"46_CR15","first-page":"1019","volume":"6","author":"W Chu","year":"2005","unstructured":"Chu, W., Ghahramani, Z.: Gaussian processes for ordinal regression. J. Mach. Learn. Res. 6(Jul), 1019\u20131041 (2005)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-59147-6_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T14:55:48Z","timestamp":1569336948000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-59147-6_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319591469","9783319591476"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-59147-6_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}