@inproceedings{08bacbc04f21448f875a1fffec682830,
title = "A double weighted Naive Bayes for multi-label classification",
abstract = "Multi-label classification is to assign an instance to multiple classes. Na{\"i}ve 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.",
keywords = "Cultural algorithm, Double weighted Naive Bayes, Multi-label classification, Naive Bayes",
author = "Xuesong Yan and Wei Li and Qinghua Wu and Sheng, {Victor S.}",
note = "Funding Information: This paper is supported by Natural Science Foundation of China (No. 61402425, 61272470, 61305087, 61440060, 41404076), the Provincial Natural Science Foundation of Hubei (No. 2013CFB320, 2015CFA065). Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 7th International Symposium on Computational Intelligence and Intelligent Systems, ISICA 2015 ; Conference date: 21-11-2015 Through 22-11-2015",
year = "2016",
doi = "10.1007/978-981-10-0356-1_40",
language = "English",
isbn = "9789811003554",
series = "Communications in Computer and Information Science",
publisher = "Springer-Verlag",
pages = "382--389",
editor = "Kangshun Li and Jin Li and Yong Liu and Aniello Castiglione",
booktitle = "Computational Intelligence and Intelligent Systems - 7th International Symposium, ISICA 2015, Revised Selected Papers",
}