- Author
- Górecki Tomasz (Adam Mickiewicz University in Poznań, Poland), Łuczak Maciej (Koszalin University of Technology, Poland)
- Title
- Stacked Regression with a Generalization of the Moore-Penrose Pseudoinverse
- Source
- Statistics in Transition, 2017, vol. 18, nr 3, s. 443-458, rys., tab., bibliogr. s. 456-458
- Keyword
- Metody klasyfikacyjne, Algorytmy genetyczne, Analiza eksperymentalna
Classification methods, Genetic algorithms, Experimental analysis - Note
- summ.
- Abstract
- In practice, it often happens that there are a number of classification methods. We are not able to clearly determine which method is optimal. We propose a combined method that allows us to consolidate information from multiple sources in a better classifier. Stacked regression (SR) is a method for forming linear combinations of different classifiers to give improved classification accuracy. The Moore-Penrose (MP) pseudoinverse is a general way to find the solution to a system of linear equations. This paper presents the use of a generalization of the MP pseudoinverse of a matrix in SR. However, for data sets with a greater number of features our exact method is computationally too slow to achieve good results: we propose a genetic approach to solve the problem. Experimental results on various real data sets demonstrate that the improvements are efficient and that this approach outperforms the classical SR method, providing a significant reduction in the mean classification error rate. (original abstract)
- Accessibility
- The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice - Full text
- Show
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- Cited by
- ISSN
- 1234-7655
- Language
- eng