Sciweavers

Share
MCS
2009
Springer

An Empirical Study of a Linear Regression Combiner on Multi-class Data Sets

9 years 6 months ago
An Empirical Study of a Linear Regression Combiner on Multi-class Data Sets
The meta-learner MLR (Multi-response Linear Regression) has been proposed as a trainable combiner for fusing heterogeneous baselevel classifiers. Although it has interesting properties, it never has been evaluated extensively up to now. This paper employs learning curves to investigate the relative performance of MLR for solving multi-class classification problems in comparison with other trainable combiners. Several strategies (namely, Reusing, Validation and Stacking) are considered for using the available data to train both the base-level classifiers and the combiner. Experimental results show that due to the limited complexity of MLR, it can outperform the other combiners for small sample sizes when the Validation or Stacking strategy is adopted. Therefore, MLR should be a preferential choice of trainable combiners when solving a multi-class task with small sample size.
Chun-Xia Zhang, Robert P. W. Duin
Added 26 Jul 2010
Updated 26 Jul 2010
Type Conference
Year 2009
Where MCS
Authors Chun-Xia Zhang, Robert P. W. Duin
Comments (0)
books