Predicting Classifier Combinations

Matthias Reif, Annika Leveringhaus, Faisal Shafait, Andreas Dengel
Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, Barcelona, Spain, SciTePress, INSTICC, 2013

Abstract:

Combining classifiers is a common technique in order to improve the performance and robustness of classification systems. However, the set of classifiers that should be combined is not obvious and either expert knowledge or a time consuming evaluation phase is required in order to achieve high accuracy values. In this paper, we present an approach of automatically selecting the set of base classifiers for combination. The method uses experience about previous classifier combinations and characteristics of datasets in order to create a prediction model. We evaluate the method on over 80 datasets. The results show that the presented method is able to reasonably predict a suitable set of base classifiers for most of the datasets.

Files:

  paper-shortened.pdf

BibTex:

@inproceedings{ REIF2013,
	Title = {Predicting Classifier Combinations},
	Author = {Matthias Reif and Annika Leveringhaus and Faisal Shafait and Andreas Dengel},
	BookTitle = {Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods},
	Year = {2013},
	Publisher = {SciTePress},
	Organization = {INSTICC}
}

     
Last modified:: 30.08.2016