{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T06:40:59Z","timestamp":1725518459568},"publisher-location":"Berlin, Heidelberg","reference-count":26,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540875352"},{"type":"electronic","value":"9783540875369"}],"license":[{"start":{"date-parts":[[2008,1,1]],"date-time":"2008-01-01T00:00:00Z","timestamp":1199145600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008]]},"DOI":"10.1007\/978-3-540-87536-9_66","type":"book-chapter","created":{"date-parts":[[2008,9,5]],"date-time":"2008-09-05T11:23:30Z","timestamp":1220613810000},"page":"642-651","source":"Crossref","is-referenced-by-count":1,"title":["FLSOM with Different Rates for Classification in Imbalanced Datasets"],"prefix":"10.1007","author":[{"given":"Iv\u00e1n","family":"Mach\u00f3n-Gonz\u00e1lez","sequence":"first","affiliation":[]},{"given":"Hilario","family":"L\u00f3pez-Garc\u00eda","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"66_CR1","unstructured":"Kubat, M., Matwin, S.: Addressing the Curse of Imbalanced Training Sets: One-sided Selection. In: Proc. 14th Int. Conf. on Machine Learning, pp. 179\u2013186 (1997)"},{"key":"66_CR2","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/11875604_14","volume-title":"Foundations of Intelligent Systems","author":"C. Vivaracho","year":"2006","unstructured":"Vivaracho, C.: Improving SVM Training by Means of NTIL When the Data Sets Are Imbalanced. In: Esposito, F., Ra\u015b, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol.\u00a04203, pp. 111\u2013120. Springer, Heidelberg (2006)"},{"key":"66_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/11492542_6","volume-title":"Pattern Recognition and Image Analysis","author":"I. Cantador","year":"2005","unstructured":"Cantador, I., Dorronsoro, J.: Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning. In: Marques, J.S., P\u00e9rez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol.\u00a03523, pp. 43\u201350. Springer, Heidelberg (2005)"},{"key":"66_CR4","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1023\/A:1007626913721","volume":"38","author":"D.R. Wilson","year":"2000","unstructured":"Wilson, D.R., Mart\u00ednez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning\u00a038, 257\u2013286 (2000)","journal-title":"Machine Learning"},{"key":"66_CR5","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1145\/1007730.1007736","volume":"6","author":"H. Guo","year":"2004","unstructured":"Guo, H., Viktor, H.L.: Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. SIGKDD Explorations\u00a06, 30\u201339 (2004)","journal-title":"SIGKDD Explorations"},{"key":"66_CR6","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. Journal of Artificial Intelligence Research\u00a016, 321\u2013357 (2002)","journal-title":"Journal of Artificial Intelligence Research"},{"key":"66_CR7","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/11731139_15","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Y. Liu","year":"2006","unstructured":"Liu, Y., An, A., Huang, X.: Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol.\u00a03918, pp. 107\u2013118. Springer, Heidelberg (2006)"},{"key":"66_CR8","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/978-3-540-30115-8_7","volume-title":"Machine Learning: ECML 2004","author":"R. Akbani","year":"2004","unstructured":"Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol.\u00a03201, pp. 39\u201350. Springer, Heidelberg (2004)"},{"key":"66_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/11550907_109","volume-title":"Artificial Neural Networks: Formal Models and Their Applications \u2013 ICANN 2005","author":"V. Soler","year":"2005","unstructured":"Soler, V., Roig, J., Prim, M.: Fuzzy Rule Extraction Using Recombined RecBF for Very-Imbalanced Datasets. In: Duch, W., Kacprzyk, J., Oja, E., Zadro\u017cny, S. (eds.) ICANN 2005. LNCS, vol.\u00a03697, pp. 685\u2013690. Springer, Heidelberg (2005)"},{"key":"66_CR10","unstructured":"Veropoulos, K., Cristianini, N., Campbell, C.: Controlling the sensitivity of support vector machines. In: International Joint Conference on Artificial Intelligence, pp. 55\u201360 (1999)"},{"key":"66_CR11","doi-asserted-by":"crossref","unstructured":"Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., Brunk, C.: Reducing misclassification costs. In: Proc. 11th Int. Conf. on Machine Learning, pp. 217\u2013225 (1994)","DOI":"10.1016\/B978-1-55860-335-6.50034-9"},{"key":"66_CR12","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1142\/S0218001493000698","volume":"7","author":"K. Woods","year":"1993","unstructured":"Woods, K., Doss, C., Bowyer, K.W., Solka, J., Priebe, C., Kegelmeyer, W.P.: Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography. International Journal of Pattern Recognition and Artificial Intelligence\u00a07, 1417\u20131436 (1993)","journal-title":"International Journal of Pattern Recognition and Artificial Intelligence"},{"issue":"1","key":"66_CR13","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1145\/1007730.1007741","volume":"6","author":"Z. Zheng","year":"2004","unstructured":"Zheng, Z., Wu, X., Srihari, R.: Feature Selection for Text Categorization on Imbalanced Data. SIGKDD Explorations\u00a06(1), 80\u201389 (2004)","journal-title":"SIGKDD Explorations"},{"key":"66_CR14","unstructured":"Fawcett, T., Provost, F.: Combining Data Mining and Machine Learning for Effective User Profile. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 8\u201313 (1996)"},{"key":"66_CR15","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1023\/A:1007452223027","volume":"30","author":"M. Kubat","year":"1998","unstructured":"Kubat, M., Holte, R.C., Matwin, S.: Machine learning for the detection of oil spills in satellite radar images. Machine Learning\u00a030, 195\u2013215 (1998)","journal-title":"Machine Learning"},{"key":"66_CR16","unstructured":"Lagus, K., Honkela, T., Kaski, S., Kohonen, T.: Self-organizing maps of document collections: a new approach to interactive exploration. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 238\u2013243 (1996)"},{"key":"66_CR17","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/5.537105","volume":"84","author":"T. Kohonen","year":"1996","unstructured":"Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proceedings of the IEEE\u00a084, 1358\u20131384 (1996)","journal-title":"Proceedings of the IEEE"},{"key":"66_CR18","unstructured":"Simula, O., Kangas, J.: Process monitoring and visualization using self-organizing maps. Neural Networks for Chemical Engineers, 371\u2013384 (1995)"},{"key":"66_CR19","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/11829898_5","volume-title":"Artificial Neural Networks in Pattern Recognition","author":"T. Villmann","year":"2006","unstructured":"Villmann, T., Seiffert, U., Schleif, F.-M., Br\u00fcss, C., Geweniger, T., Hammer, B.: Fuzzy Labeled Self-Organizing Map with Label-Ajusted Prototypes. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS (LNAI), vol.\u00a04087, pp. 46\u201356. Springer, Heidelberg (2006)"},{"key":"66_CR20","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-97610-0","volume-title":"Self-organizing maps","author":"T. Kohonen","year":"1995","unstructured":"Kohonen, T.: Self-organizing maps, 3rd extended edn. 2001. Springer, Berlin (1995)","edition":"3"},{"issue":"3","key":"66_CR21","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/72.846731","volume":"11","author":"J. Vesanto","year":"2000","unstructured":"Vesanto, J., Alhoniemi, E.: Clustering of the Self-Organizing Map. IEEE Transactions Transactions on Neural Networks\u00a011(3), 586\u2013600 (2000)","journal-title":"IEEE Transactions Transactions on Neural Networks"},{"issue":"3","key":"66_CR22","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.engappai.2004.03.004","volume":"17","author":"H. L\u00f3pez","year":"2004","unstructured":"L\u00f3pez, H., Mach\u00f3n, I.: Self-organizing map and clustering for wastewater treatment monitoring. Engineering Applications of Artificial Intelligence\u00a017(3), 215\u2013225 (2004)","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1","key":"66_CR23","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.engappai.2005.05.008","volume":"19","author":"I. Mach\u00f3n","year":"2006","unstructured":"Mach\u00f3n, I., L\u00f3pez, H.: End-point detection of the aerobic phase in a biological reactor using SOM and clustering algorithms. Engineering Applications of Artificial Intelligence\u00a019(1), 19\u201328 (2006)","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"66_CR24","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/B978-044450270-4\/50024-3","volume-title":"Kohonen Maps","author":"T. Heskes","year":"1999","unstructured":"Heskes, T.: Energy functions for self-organizing maps. In: Oja, E., Kaski, S. (eds.) Kohonen Maps, pp. 303\u2013316. Elsevier, Amsterdam (1999)"},{"key":"66_CR25","unstructured":"Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http:\/\/mlearn.ics.uci.edu\/MLSummary.html"},{"key":"66_CR26","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1007\/978-3-540-24694-7_32","volume-title":"MICAI 2004: Advances in Artificial Intelligence","author":"R. Prati","year":"2004","unstructured":"Prati, R., Batista, G., Monard, M.: Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol.\u00a02972, pp. 312\u2013321. Springer, Heidelberg (2004)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks - ICANN 2008"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-540-87536-9_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T21:59:38Z","timestamp":1631743178000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-540-87536-9_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008]]},"ISBN":["9783540875352","9783540875369"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-540-87536-9_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2008]]}}}