Name
Abstract | ||
|---|---|---|
In this study, a hybrid robust support vector machine for regression is proposed to deal with training data sets with outliers. The proposed approach consists of two stages of strategies. The first stage is for data preprocessing and a support vector machine for regression is used to filter out outliers in the training data set. Since the outliers in the training data set are removed, the concept of robust statistic is not needed for reducing the outliers' effects in the later stage. Then, the training data set except for outliers, called as the reduced training data set, is directly used in training the non-robust least squares support vector machines for regression (LS-SVMR) or the non-robust support vector regression networks (SVRNs) in the second stage. Consequently, the learning mechanism of the proposed approach is much easier than that of the robust support vector regression networks (RSVRNs) approach and of the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach with non-robust LS-SVMR is superior to the weighted LS-SVMR approach when the outliers exist. Moreover, the performance of the proposed approach with non-robust SVRNs is also superior to the RSVRNs approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.asoc.2009.10.017 | Appl. Soft Comput. |
Keywords | Field | DocType |
robust support vector regression,training data set,reduced training data,least squares support vector machines for regression (ls-svmr),outliers,weighted ls-svmr approach,training data,robust support vector regression networks,weighted ls-svmr,rsvrns approach,squares support vector machine,hybrid robust support vector,non-robust support vector regression,support vector machines for regression,least squares support vector machine,support vector regression,robust statistics,data preprocessing,support vector machine | Structured support vector machine,Data mining,Least trimmed squares,Data pre-processing,Robust regression,Artificial intelligence,Least squares support vector machine,Pattern recognition,Support vector machine,Outlier,Relevance vector machine,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 1 | Applied Soft Computing Journal |
Citations | PageRank | References |
28 | 0.89 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen-Chia Chuang | 1 | 540 | 34.02 |
| Zne-Jung Lee | 2 | 940 | 43.45 |