caa a22 4500 605477965 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-014-5477-5 doi (NATIONALLICENCE)springer-10.1007/s10994-014-5477-5 Pruning of Error Correcting Output Codes by optimization of accuracy-diversity trade off [Elektronische Daten] [Süreyya Özöğür-Akyüz, Terry Windeatt, Raymond Smith] Ensemble learning is a method of combining learners to obtain more reliable and accurate predictions in supervised and unsupervised learning. However, the ensemble sizes are sometimes unnecessarily large which leads to additional memory usage, computational overhead and decreased effectiveness. To overcome such side effects, pruning algorithms have been developed; since this is a combinatorial problem, finding the exact subset of ensembles is computationally infeasible. Different types of heuristic algorithms have developed to obtain an approximate solution but they lack a theoretical guarantee. Error Correcting Output Code (ECOC) is one of the well-known ensemble techniques for multiclass classification which combines the outputs of binary base learners to predict the classes for multiclass data. In this paper, we propose a novel approach for pruning the ECOC matrix by utilizing accuracy and diversity information simultaneously. All existing pruning methods need the size of the ensemble as a parameter, so the performance of the pruning methods depends on the size of the ensemble. Our unparametrized pruning method is novel as being independent of the size of ensemble. Experimental results show that our pruning method is mostly better than other existing approaches. The Author(s), 2014 Ensemble learning nationallicence Ensemble pruning nationallicence Error Correcting Output Codes nationallicence DC programming nationallicence Support vector machines nationallicence Integer programming nationallicence Özöğür-Akyüz Süreyya Center for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Surrey, UK aut Windeatt Terry Center for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Surrey, UK aut Smith Raymond Center for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Surrey, UK aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 253-269 0885-6125 101:1-3<253 2015 101 10994 https://doi.org/10.1007/s10994-014-5477-5 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/s10994-014-5477-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Özöğür-Akyüz Süreyya Center for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Surrey, UK aut NATIONALLICENCE 700 1- Windeatt Terry Center for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Surrey, UK aut NATIONALLICENCE 700 1- Smith Raymond Center for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Surrey, UK aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 253-269 0885-6125 101:1-3<253 2015 101 10994