A Double Weighted Naive Bayes with Niching Cultural Algorithm for Multi-Label Classification

作     者：Yan, Xuesong Wu, Qinghua Sheng, Victor S. 

作者机构：Chin Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China WuHan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Hubei Peoples R China Univ Cent Arkansas Dept Comp Sci Conway AR 72035 USA 

出 版 物：《INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE》 (国际图形识别与人工智能杂志)

年 卷 期：2016年第30卷第6期

页      面：1650013-1650013页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of China [61402425, 61272470, 61305087, 61440060, 41404076] US National Science Foundation [IIS-1115417] Provincial Natural Science Foundation of Hubei [2015CFA065] Div Of Information & Intelligent Systems Direct For Computer & Info Scie & Enginr Funding Source: National Science Foundation 

主　　题：Multi-label classification Naive Bayes double weighted naive bayes cultural algorithm niching cultural algorithm 

摘      要：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 (NLA) to determine the weight con figuration automatically. Our experimental results show that our proposed DWNB outperforms NB and its extensions significantly in multi-label classification.