Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Small-Sample Error Estimation for Bagged Classification Rules | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 548906 12 pages doi 2010 548906 Research Article Small-Sample Error Estimation for Bagged Classification Rules T. T. Vu and U. M. Braga-Neto Department of Electrical and Computer Engineering Texas A M University College Station TX 77843-3128 USA Correspondence should be addressed to U. M. Braga-Neto ulisses@ Received 2 April 2010 Accepted 16 July 2010 Academic Editor Harri Lahdesmaki Copyright 2010 T. T. Vu and U. M. Braga-Neto. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Application of ensemble classification rules in genomics and proteomics has become increasingly common. However the problem of error estimation for these classification rules particularly for bagging under the small-sample settings prevalent in genomics and proteomics is not well understood. Breiman proposed the out-of-bag method for estimating statistics of bagged classifiers which was subsequently applied by other authors to estimate the classification error. In this paper we give an explicit definition of the out-of-bag estimator that is intended to remove estimator bias by formulating carefully how the error count is normalized. We also report the results of an extensive simulation study of bagging of common classification rules including LDA 3NN and CART applied on both synthetic and real patient data corresponding to the use of common error estimators such as resubstitution leave-one-out cross-validation basic bootstrap bootstrap 632 bootstrap 632 plus bolstering semi-bolstering in addition to the out-of-bag estimator. The results from the numerical experiments indicated that the performance of the out-of-bag estimator is very similar to that of leave-one-out in particular the out-of-bag estimator is .
