文章基本信息

标题：STACKED REGRESSION WITH A GENERALIZATION OF THE MOORE-PENROSE PSEUDOINVERSE
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作者：Tomasz Górecki ; Maciej Łuczak
期刊名称：Statistics in Transition
印刷版ISSN：1234-7655
电子版ISSN：2450-0291
出版年度：2017
卷号：18
期号：3
页码：443-458
DOI：10.21307/stattrans-2016-080
出版社：Exeley Inc.
摘要：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.
关键词：stacked regression;genetic algorithm;Moore-Penrose pseudoinverse