 Sequential combining in discriminant analysisT. G�recki Journal of Applied Statistics, 2015, vol. 42, issue 2, 398-408 Abstract: In practice, it often happens that we have a number of base methods of classification. We are not able to clearly determine which method is optimal in the sense of the smallest error rate. Then we have a combined method that allows us to consolidate information from multiple sources in a better classifier. I propose a different approach, a sequential approach. Sequentiality is understood here in the sense of adding posterior probabilities to the original data set and so created data are used during classification process. We combine posterior probabilities obtained from base classifiers using all combining methods. Finally, we combine these probabilities using a mean combining method. To the original data set we add obtained posterior probabilities as additional features. In each step we change our additional probabilities to achieve the minimum error rate for base methods. Experimental results on different data sets demonstrate that the method is efficient and that this approach outperforms base methods providing a reduction in the mean classification error rate. Date: 2015 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:42:y:2015:i:2:p:398-408 Ordering information: This journal article can be ordered from DOI: 10.1080/02664763.2014.951605 Access Statistics for this article Journal of Applied Statistics is currently edited by Robert Aykroyd More articles in Journal of Applied Statistics from Taylor & Francis Journals |
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