{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T13:05:50Z","timestamp":1750338350630},"publisher-location":"Berlin, Heidelberg","reference-count":26,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642410123"},{"type":"electronic","value":"9783642410130"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013]]},"DOI":"10.1007\/978-3-642-41013-0_22","type":"book-chapter","created":{"date-parts":[[2013,9,24]],"date-time":"2013-09-24T22:06:22Z","timestamp":1380060382000},"page":"213-222","source":"Crossref","is-referenced-by-count":10,"title":["Impact of Sampling on Neural Network Classification Performance in the Context of Repeat Movie Viewing"],"prefix":"10.1007","author":[{"given":"Elena","family":"Fitkov-Norris","sequence":"first","affiliation":[]},{"given":"Sakinat Oluwabukonla","family":"Folorunso","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"issue":"1","key":"22_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1007730.1007733","volume":"6","author":"N.V. Chawla","year":"2004","unstructured":"Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: Special Issue on Learning from Imbalanced Data Sets. SIGKDD Explor. Newsl.\u00a06(1), 1\u20136 (2004)","journal-title":"SIGKDD Explor. Newsl."},{"issue":"5","key":"22_CR2","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N. Japkowicz","year":"2002","unstructured":"Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Intell. Data. Anal.\u00a06(5), 429\u2013449 (2002)","journal-title":"Intell. Data. Anal."},{"key":"22_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-21219-2_1","volume-title":"Hybrid Artificial Intelligent Systems","author":"A. Fern\u00e1ndez","year":"2011","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Herrera, F.: Addressing the Classification with Imbalanced Data: Open Problems and New Challenges on Class Distribution. In: Corchado, E., Kurzy\u0144ski, M., Wo\u017aniak, M. (eds.) HAIS 2011, Part I. LNCS, vol.\u00a06678, pp. 1\u201310. Springer, Heidelberg (2011)"},{"key":"22_CR4","unstructured":"Pearson, R., Goney, G., Shwaber, J.: Imbalanced Clustering of Microarray Time-Series. In: Fawcett, T., Mishra, S. (eds.) 12th International Conference on Machine Learning Workshop on Learning from Imbalanced Datasets II, Washington DC, vol.\u00a03 (2003)"},{"key":"22_CR5","unstructured":"Kubat, M., Matwin, S.: Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. In: 14th International Conference on Machine Learning, Nashville, Tennessee, USA, pp. 179\u2013186 (1997)"},{"key":"22_CR6","first-page":"139","volume":"2","author":"L.M. Manevitz","year":"2002","unstructured":"Manevitz, L.M., Yousef, M.: One-Class SVMs for Document Classification. JMLR\u00a02, 139\u2013154 (2002)","journal-title":"JMLR"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Thai-Nghe, N., Busche, A., Schmidt-Thieme, L.: Improving Academic Performance Prediction by Dealing with Class Imbalance. In: 9th IEEE International Conference on Intelligent Systems Design and Applications, Pisa, Italy, pp. 878\u2013883 (2009)","DOI":"10.1109\/ISDA.2009.15"},{"issue":"1","key":"22_CR8","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"G.E.A.P.A. Batista","year":"2004","unstructured":"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.\u00a06(1), 20\u201329 (2004)","journal-title":"SIGKDD Explor. Newsl."},{"key":"22_CR9","unstructured":"Folorunso, S.O., Adeyemo, A.B.: Theoretical Comparison of Undersampling Techniques Against Their Underlying Data Reduction Techniques. In: EIE 2nd International Conference Computing, Energy, Networking, Robotics and Telecommunications (EIECON 2012), Lagos, Nigeria, pp. 92\u201397 (2012)"},{"issue":"1","key":"22_CR10","first-page":"25","volume":"30","author":"S. Kotsiantis","year":"2006","unstructured":"Kotsiantis, S., Kanellopoulos, D., Pintelas, P.: Handling Imbalanced Datasets: A Review. GESTS International Transactions on Computer Science and Engineering\u00a030(1), 25\u201336 (2006)","journal-title":"GESTS International Transactions on Computer Science and Engineering"},{"issue":"1","key":"22_CR11","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/TKDE.2006.17","volume":"18","author":"Z.-H. Zhou","year":"2006","unstructured":"Zhou, Z.-H., Liu, X.-Y.: Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. IEEE T. Knowl. Data. En.\u00a018(1), 63\u201377 (2006)","journal-title":"IEEE T. Knowl. Data. En."},{"issue":"2","key":"22_CR12","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neunet.2007.12.031","volume":"21","author":"M.A. Mazurowski","year":"2008","unstructured":"Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training Neural Network Classifiers for Medical Decision Making: The Effects of Imbalanced Datasets on Classification Performance. Neural Networks\u00a021(2), 427\u2013436 (2008)","journal-title":"Neural Networks"},{"issue":"1","key":"22_CR13","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.ijforecast.2011.07.006","volume":"28","author":"S.F. Crone","year":"2011","unstructured":"Crone, S.F., Finlay, S.: Instance Sampling in Credit Scoring: an Empirical Study of Sample Size and Balancing. Int. J. Forecasting\u00a028(1), 224\u2013238 (2011)","journal-title":"Int. J. Forecasting"},{"issue":"3","key":"22_CR14","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s10824-008-9072-0","volume":"32","author":"A. Collins","year":"2008","unstructured":"Collins, A., Hand, C., Linnell, M.: Analyzing Repeat Consumption of Identical Cultural Goods: Some Exploratory Evidence from Moviegoing. J. Cult. Econ.\u00a032(3), 187\u2013199 (2008)","journal-title":"J. Cult. Econ."},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Sawhney, M., Eliashberg, J.: A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures. Market. Sci., 113\u2013131 (2001)","DOI":"10.1287\/mksc.15.2.113"},{"issue":"2","key":"22_CR16","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.eswa.2005.07.018","volume":"30","author":"R. Sharda","year":"2006","unstructured":"Sharda, R., Delen, D.: Predicting Box-Office Success of Motion Pictures with Neural Networks. Expert Syst. Appl.\u00a030(2), 243\u2013254 (2006)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"22_CR17","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.eswa.2007.10.005","volume":"36","author":"M. Paliwal","year":"2009","unstructured":"Paliwal, M., Kumar, U.A.: Neural Networks and Statistical Techniques: A Review of Applications. Expert Syst. Appl.\u00a036(1), 2\u201317 (2009)","journal-title":"Expert Syst. Appl."},{"key":"22_CR18","series-title":"CCIS","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-642-32909-8_35","volume-title":"Engineering Applications of Neural Networks","author":"E. Fitkov-Norris","year":"2012","unstructured":"Fitkov-Norris, E., Vahid, S., Hand, C.: Evaluating the Impact of Categorical Data Encoding and Scaling on Neural Network Classification Performance: The Case of Repeat Consumption of Identical Cultural Goods. In: Jayne, C., Yue, S., Iliadis, L. (eds.) EANN 2012. CCIS, vol.\u00a0311, pp. 343\u2013352. Springer, Heidelberg (2012)"},{"issue":"3","key":"22_CR19","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/TIT.1968.1054155","volume":"14","author":"P.E. Hart","year":"1968","unstructured":"Hart, P.E.: The Condensed Nearest Neighbor Rule. IEEE T. Inform. Theory\u00a014(3), 515\u2013516 (1968)","journal-title":"IEEE T. Inform. Theory"},{"key":"22_CR20","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/3-540-48229-6_9","volume-title":"Artificial Intelligence in Medicine","author":"J. Laurikkala","year":"2001","unstructured":"Laurikkala, J.: Improving Identification of Difficult Small Classes by Balancing Class Distribution. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol.\u00a02101, pp. 63\u201366. Springer, Heidelberg (2001)"},{"issue":"6","key":"22_CR21","first-page":"769","volume":"11","author":"I. Tomek","year":"1976","unstructured":"Tomek, I.: Two Modifications of CNN. IEEE T. Syst. Man. Cyb.\u00a011(6), 769\u2013772 (1976)","journal-title":"IEEE T. Syst. Man. Cyb."},{"issue":"3","key":"22_CR22","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"SMC-2","author":"D.L. Wilson","year":"1972","unstructured":"Wilson, D.L.: Asymptotic Properties of Nearest Neighbor Rules Using Edited Data. IEEE T. Syst. Man. Cyb.\u00a0SMC-2(3), 408\u2013421 (1972)","journal-title":"IEEE T. Syst. Man. Cyb."},{"key":"22_CR23","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"N.V. 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.\u00a016, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"2","key":"22_CR24","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10115-011-0465-6","volume":"33","author":"E. Ramentol","year":"2011","unstructured":"Ramentol, E., Caballero, Y., Bello, R., Herrera, F.: SMOTE-RSB*: a Hybrid Preprocessing Approach Based on Oversampling and Undersampling for High Imbalanced Data-Sets Using SMOTE and Rough Sets Theory. Knowl. Inf. Syst.\u00a033(2), 245\u2013265 (2011)","journal-title":"Knowl. Inf. Syst."},{"issue":"3","key":"22_CR25","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1162\/evco.2009.17.3.275","volume":"17","author":"S. Garc\u00eda","year":"2009","unstructured":"Garc\u00eda, S., Herrera, F.: Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy. Evol. Comput.\u00a017(3), 275\u2013306 (2009)","journal-title":"Evol. Comput."},{"issue":"10","key":"22_CR26","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TNN.2010.2066988","volume":"21","author":"S. Chen","year":"2010","unstructured":"Chen, S., He, H., Garcia, E.A.: RAMOBoost: Ranked Minority Oversampling in Boosting. IEEE T. Neural Networ.\u00a021(10), 1624\u20131642 (2010)","journal-title":"IEEE T. Neural Networ"}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-41013-0_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T10:49:39Z","timestamp":1558090179000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-41013-0_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"ISBN":["9783642410123","9783642410130"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-41013-0_22","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2013]]}}}