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A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Double Weighted Naive Bayes for Multi-label Classification

Authors : Xuesong Yan, Wei Li, Qinghua Wu, Victor S. Sheng

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

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Abstract

Multi-label classification is to assign an instance to multiple classes. Naive Bayes (NB) is one of the most popular algorithms for pattern recognition and classification. It has a high performance in single label classification. It is naturally extended for multi-label classification under the assumption of label independence. As we know, NB is based on a simple but unrealistic assumption that attributes are conditionally independent given the class. Therefore, a double weighted NB (DWNB) is proposed to demonstrate the influences of predicting different labels based on different attributes. Our DWNB utilizes the niching cultural algorithm to determine the weight configuration automatically. Our experimental results show that our proposed DWNB outperforms NB and its extensions significantly in multi-label classification.

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Metadata
Title
A Double Weighted Naive Bayes for Multi-label Classification
Authors
Xuesong Yan
Wei Li
Qinghua Wu
Victor S. Sheng
Copyright Year
2016
Publisher
Springer Singapore
Book
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-10-0355-4

Electronic ISBN: 978-981-10-0356-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-0356-1_40

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