caa a22 4500 605469369 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s00500-014-1407-5 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1407-5 Boosted SVM with active learning strategy for imbalanced data [Elektronische Daten] [Maciej Zięba, Jakub Tomczak] In this work, we introduce a novel training method for constructing boosted Support Vector Machines (SVMs) directly from imbalanced data. The proposed solution incorporates the mechanisms of active learning strategy to eliminate redundant instances and more properly estimate misclassification costs for each of the base SVMs in the committee. To evaluate our approach, we make comprehensive experimental studies on the set of $$44$$ 44 benchmark datasets with various types of imbalance ratio. In addition, we present application of our method to the real-life decision problem related to the short-term loans repayment prediction. The Author(s), 2014 Imbalanced data nationallicence Boosted SVM nationallicence Active learning nationallicence Zięba Maciej Faculty of Computer Science and Management, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland aut Tomczak Jakub Faculty of Computer Science and Management, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland aut Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3357-3368 1432-7643 19:12<3357 2015 19 500 https://doi.org/10.1007/s00500-014-1407-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1407-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zięba Maciej Faculty of Computer Science and Management, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland aut NATIONALLICENCE 700 1- Tomczak Jakub Faculty of Computer Science and Management, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3357-3368 1432-7643 19:12<3357 2015 19 500