Genetic Algorithm Based Optimization for AdaBoost Export

@inproceedings{citeulike:4960157, abstract = {AdaBoost was proposed as an efficient algorithm of the ensemble learning field, it selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy can not be avoided. We proposed a post optimization procedure for the found classifiers and their coefficients based on Genetic Algorithm, which removes the redundancy classifiers and leads to shorter final classifiers and a speedup of classification. Our algorithm is tested on the UCI benchmark data sets, fewer weak classifiers and faster classification compared with conventional AdaBoost algorithm is experienced.}, address = {Washington, DC, USA}, author = {Dezhen, Zhang and Kai, Yang}, booktitle = {CSSE '08: Proceedings of the 2008 International Conference on Computer Science and Software Engineering}, citeulike-article-id = {4960157}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1468661}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/CSSE.2008.1040}, doi = {10.1109/CSSE.2008.1040}, isbn = {978-0-7695-3336-0}, keywords = {boosting, classification, genetic}, pages = {1044--1047}, posted-at = {2009-06-25 20:15:45}, priority = {0}, publisher = {IEEE Computer Society}, title = {Genetic Algorithm Based Optimization for AdaBoost}, url = {http://dx.doi.org/10.1109/CSSE.2008.1040}, year = {2008} }

TY - CONF ID - citeulike:4960157 L3 - citeulike-article-id:4960157 N2 - AdaBoost was proposed as an efficient algorithm of the ensemble learning field, it selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy can not be avoided. We proposed a post optimization procedure for the found classifiers and their coefficients based on Genetic Algorithm, which removes the redundancy classifiers and leads to shorter final classifiers and a speedup of classification. Our algorithm is tested on the UCI benchmark data sets, fewer weak classifiers and faster classification compared with conventional AdaBoost algorithm is experienced. SN - 978-0-7695-3336-0 AD - Washington, DC, USA EP - 1047 TI - Genetic Algorithm Based Optimization for AdaBoost PB - IEEE Computer Society T2 - CSSE '08: Proceedings of the 2008 International Conference on Computer Science and Software Engineering SP - 1044 KW - boosting KW - classification KW - genetic AU - Dezhen, Zhang AU - Kai, Yang PY - 2008/// UR - http://dx.doi.org/10.1109/CSSE.2008.1040 ER -