{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T14:58:38Z","timestamp":1761663518436,"version":"3.41.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319461816"},{"type":"electronic","value":"9783319461823"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","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":[[2016]]},"DOI":"10.1007\/978-3-319-46182-3_13","type":"book-chapter","created":{"date-parts":[[2016,9,8]],"date-time":"2016-09-08T05:19:28Z","timestamp":1473311968000},"page":"150-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Towards Effective Classification of Imbalanced Data with Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Vidwath","family":"Raj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sven","family":"Magg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Wermter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,9,9]]},"reference":[{"issue":"4","key":"13_CR1","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.patrec.2012.09.003","volume":"34","author":"R Alejo","year":"2013","unstructured":"Alejo, R., Valdovinos, R.M., Garc\u00eda, V., Pacheco-Sanchez, J.: A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios. Pattern Recogn. Lett. 34(4), 380\u2013388 (2013)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"13_CR2","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1111\/j.1540-5915.1999.tb00902.x","volume":"30","author":"VL Berardi","year":"1999","unstructured":"Berardi, V.L., Zhang, G.P.: The effect of misclassification costs on neural network classifiers. Decis. Sci. 30(3), 659\u2013682 (1999)","journal-title":"Decis. Sci."},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy), vol. 4, p. 3, Austin, TX (2010)","DOI":"10.25080\/Majora-92bf1922-003"},{"key":"13_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/978-3-642-40319-4_39","volume-title":"Trends and Applications in Knowledge Discovery and Data Mining","author":"P Cao","year":"2013","unstructured":"Cao, P., Zhao, D., Za\u00efane, O.R.: A PSO-based cost-sensitive neural network for imbalanced data classification. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013. LNCS, vol. 7867, pp. 452\u2013463. Springer, Heidelberg (2013)"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Castro, C.L., de P\u00e1dua Braga, A.: Artificial neural networks learning in ROC space. In: IJCCI, pp. 484\u2013489 (2009)","DOI":"10.5220\/0002324404840489"},{"key":"13_CR6","unstructured":"Chan, P.K., Stolfo, S.J.: Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection. In: KDD, vol. 1998, pp. 164\u2013168 (1998)"},{"key":"13_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."},{"key":"13_CR8","unstructured":"Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR time series classification archive, July 2015. www.cs.ucr.edu\/~eamonn\/time_series_data\/"},{"key":"13_CR9","unstructured":"Khan, S.H., Bennamoun, M., Sohel, F., Togneri, R.: Cost sensitive learning of deep feature representations from imbalanced data (2015). arXiv preprint arXiv:1508.03422"},{"issue":"5","key":"13_CR10","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1109\/TNN.2010.2042730","volume":"21","author":"TM Khoshgoftaar","year":"2010","unstructured":"Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors. IEEE Trans. Neural Netw. 21(5), 813\u2013830 (2010)","journal-title":"IEEE Trans. Neural Netw."},{"key":"13_CR11","unstructured":"Kukar, M., Kononenko, I., et al.: Cost-sensitive learning with neural networks. In: ECAI, pp. 445\u2013449. Citeseer (1998)"},{"key":"13_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"YA LeCun","year":"2012","unstructured":"LeCun, Y.A., Bottou, L., Orr, G.B., M\u00fcller, K.-R.: Efficient backprop. In: Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 1524, pp. 9\u201348. Springer, Heidelberg (2012)"},{"key":"13_CR13","unstructured":"Liu, X.Y., Zhou, Z.H.: The influence of class imbalance on cost-sensitive learning: an empirical study. In: Sixth International Conference on Data Mining, 2006, ICDM 2006, pp. 970\u2013974. IEEE (2006)"},{"issue":"2","key":"13_CR14","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1023\/B:APIN.0000033632.42843.17","volume":"21","author":"YL Murphey","year":"2004","unstructured":"Murphey, Y.L., Guo, H., Feldkamp, L.A.: Neural learning from unbalanced data. Appl. Intell. 21(2), 117\u2013128 (2004)","journal-title":"Appl. Intell."},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 78. ACM (2004)","DOI":"10.1145\/1015330.1015435"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Raj, V.: Towards effective classification of imbalanced data with convolutional neural networks. Master\u2019s thesis, Department of Informatics, University of Hamburg, Vogt-Koelln-Str. 22527 Hamburg, Germany, April 2016","DOI":"10.1007\/978-3-319-46182-3_13"},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"issue":"12","key":"13_CR18","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun, Y., Kamel, M.S., Wong, A.K., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358\u20133378 (2007)","journal-title":"Pattern Recogn."},{"issue":"1","key":"13_CR19","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/0031-3203(93)90099-I","volume":"26","author":"J Wang","year":"1993","unstructured":"Wang, J., Jean, J.: Resolving multifont character confusion with neural networks. Pattern Recogn. 26(1), 175\u2013187 (1993)","journal-title":"Pattern Recogn."},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"3","author":"DL Wilson","year":"1972","unstructured":"Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. 3, 408\u2013421 (1972)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"13_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/978-3-319-08010-9_33","volume-title":"Web-Age Information Management","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298\u2013310. Springer, Heidelberg (2014)"},{"issue":"1","key":"13_CR22","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/TKDE.2006.17","volume":"18","author":"ZH Zhou","year":"2006","unstructured":"Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 18(1), 63\u201377 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks in Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46182-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T17:23:40Z","timestamp":1749576220000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-46182-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319461816","9783319461823"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46182-3_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"9 September 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ANNPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IAPR Workshop on Artificial Neural Networks in Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ulm","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"annpr2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}