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Effective Monotone Knowledge Integration in Kernel Support Vector Machines Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Single Classifier Selection for Ensemble Learning

Authors : Guangtao Wang, Xiaomei Yang, Xiaoyan Zhu

Published in: Advanced Data Mining and Applications

Publisher: Springer International Publishing

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Abstract

Ensemble classification is one of representative learning techniques in the field of machine learning, which combines a set of single classifiers together aiming at achieving better classification performance. Not every arbitrary set of single classifiers can obtain a good ensemble classifier. The efficient and necessary condition to construct an accurate ensemble classifier is that the single classifiers should be accurate and diverse. In this paper, we first formally give the definitions of accurate and diverse classifiers and put forward metrics to quantify the accuracy and diversity of the single classifiers; afterwards, we propose a novel parameter-free method to pick up a set of accurate and diverse single classifiers for ensemble. The experimental results on real world data sets show the effectiveness of the proposed method which could improve the performance of the representative ensemble classifier Bagging.

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Footnotes
1
In the field of ensemble learning, the independent single classifier is generally called diverse one, and the independence is also named diversity.
 
2
In the table, “F”, “I” and “T” denote the numbers of features, instances and target concept values, respectively.
 
3
In the table, “\( acc_{RF}\)” “\(acc_{BD}(acc_{BN})\)” and “\( acc_{after} \)” denote the classification accuracy of RandomForest, Boosting+DT(Boosting+NB) and Bagging filtered by Algorithm 1.
 
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Metadata
Title
Single Classifier Selection for Ensemble Learning
Authors
Guangtao Wang
Xiaomei Yang
Xiaoyan Zhu
Copyright Year
2016
Book
Advanced Data Mining and Applications

Print ISBN: 978-3-319-49585-9

Electronic ISBN: 978-3-319-49586-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49586-6_21

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