Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/3019
Title: Integrative machine learning approach for multi-class SCOP protein fold classification
Authors: Tan, A C
Gilbert, D
Deville, Y
Issue Date: 2003
Publisher: GCB
Citation: Proceedings of the German Conference on Bioinformatics (GCB 2003), Neuherberg, 12-14 October 2003
Abstract: Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP classification, most discriminative machine learning suffers the well-known ‘False Positives ’ problem when learning over these types of problems. We have devised eKISS, an ensemble machine learning specifically designed to increase the coverage of positive examples when learning under multiclass imbalanced data sets. We have applied eKISS to classify 25 SCOP folds and show that our learning system improved over classical learning methods.
URI: http://bura.brunel.ac.uk/handle/2438/3019
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
tanGCB2003.pdf57.14 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.