Title of article :
Empirical analysis of support vector machine ensemble classifiers
Author/Authors :
Wang، نويسنده , , Shi-jin and Mathew، نويسنده , , Avin and Chen، نويسنده , , Yan and Xi، نويسنده , , Lifeng and Ma، نويسنده , , Lin and Lee، نويسنده , , Jay، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
Pages :
11
From page :
6466
To page :
6476
Abstract :
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.
Keywords :
AdaBoost , Ensemble classification , Support vector machines (SVMs) , Bagging
Journal title :
Expert Systems with Applications
Serial Year :
2009
Journal title :
Expert Systems with Applications
Record number :
2346227
Link To Document :
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