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Báo cáo hóa học: " Research Article Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?"

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 Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data? | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2009 Article ID 158368 10 pages doi 10.1155 2009 158368 Research Article Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data 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@ece.tamu.edu Received 1 August 2008 Revised 4 December 2008 Accepted 19 January 2009 Recommended by Yufei Huang There has been considerable interest recently in the application of bagging in the classification of both gene-expression data and protein-abundance mass spectrometry data. The approach is often justified by the improvement it produces on the performance of unstable overfitting classification rules under small-sample situations. However the question of real practical interest is whether the ensemble scheme will improve performance of those classifiers sufficiently to beat the performance of single stable nonoverfitting classifiers in the case of small-sample genomic and proteomic data sets. To investigate that question we conducted a detailed empirical study using publicly-available data sets from published genomic and proteomic studies. We observed that under t-test and RELIEF filter-based feature selection bagging generally does a good job of improving the performance of unstable overfitting classifiers such as CART decision trees and neural networks but that improvement was not sufficient to beat the performance of single stable nonoverfitting classifiers such as diagonal and plain linear discriminant analysis or 3-nearest neighbors. Furthermore as expected the ensemble method did not improve the performance of these classifiers significantly. Representative experimental results are presented and discussed in this work. Copyright 2009 T. T. Vu and U. M. Braga-Neto. This is an open access article distributed .