NDLI: Benchmarking local classification methods

Content Provider	Springer Nature Link
Author	Bischl, Bernd Schiffner, Julia Weihs, Claus
Copyright Year	2013
Abstract	In recent years in the fields of statistics and machine learning an increasing amount of so called local classification methods has been developed. Local approaches to classification are not new, but have lately become popular. Well-known examples are the $$k$$ nearest neighbors method and classification trees. However, in most publications on this topic the term “local” is used without further explanation of its particular meaning. Only little is known about the properties of local methods and the types of classification problems for which they may be beneficial. We explain the basic principles and introduce the most important variants of local methods. To our knowledge there are very few extensive studies in the literature that compare several types of local methods and global methods across many data sets. In order to assess their performance we conduct a benchmark study on real-world and synthetic tasks. We cluster data sets and considered learning algorithms with regard to the obtained performance structures and try to relate our theoretical considerations and intuitions to these results. We also address some general issues of benchmark studies and cover some pitfalls, extensions and improvements.
Starting Page	2599
Ending Page	2619
Page Count	21
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	28
Issue Number	6
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-05-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Local classification methods Benchmark study Machine learning Model selection Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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