Empirical analysis of support vector machine ensemble classifiers
Wang, Shi-jin , Mathew, Avin D., Chen, Yan , Xi, Li-feng , Ma, Lin, & Lee, Jay (2009) Empirical analysis of support vector machine ensemble classifiers. Expert Systems with Applications, 36(3 Pt2), pp. 6466-6476.
|Accepted Version (PDF 477kB) |
Administrators only | Request a copy from author
Ensemble classification – combining the results of a set of base learners – has received much attention in the machine learning community and has demonstrated promising capabilities in improving classification accuracy. Compared with neural network or decision tree ensembles, there is no comprehensive empirical research in support vector machine (SVM) ensembles. To fill this void, this paper analyses and compares SVM ensembles with four different ensemble constructing techniques, namely bagging, AdaBoost, Arc-X4 and a modified AdaBoost. Twenty real-world data sets from the UCI repository are used as benchmarks to evaluate and compare the performance of these SVM ensemble classifiers by their classification accuracy. Different kernel functions and different numbers of base SVM learners are tested in the ensembles. The experimental results show that although SVM ensembles are not always better than a single SVM, the SVM bagged ensemble performs as well or better than other methods with a relatively higher generality, particularly SVMs with a polynomial kernel function. Finally, an industrial case study of gear defect detection is conducted to validate the empirical analysis results.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Journal Article|
|Additional Information:||For more information, please refer to the journal's website (see hypertext link) or contact the author. Author contact details:firstname.lastname@example.org|
|Keywords:||Ensemble classification, Support vector machines (SVMs), Boosting, AdaBoost, Bagging|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)|
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)
|Divisions:||Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)|
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2008 Elsevier Ltd|
|Copyright Statement:||NOTICE: this is the author’s version of a work that was accepted for publication in [Expert Systems with Applications]. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in [Expert Systems with Applications], [VOL 36, ISSUE 3 Pt2, (2008)] DOI 10.1016/j.eswa.2008.07.041|
|Deposited On:||16 Feb 2009 11:51|
|Last Modified:||23 Apr 2012 08:18|
Repository Staff Only: item control page