.png){kind=icon}
返回
Attribute and instance weighted naive Bayes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107674.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Naive Bayes (NB) continues to be one of the top 10 data mining algorithms, but its conditional independence assumption rarely holds true in real-world applications. Therefore, many different categories of improved approaches, including attribute weighting and instance weighting, have been proposed to alleviate this assumption. However, few of these approaches simultaneously pay attention to attribute weighting and instance weighting. In this study, we propose a new improved model called attribute and instance weighted naive Bayes (AIWNB), which combines attribute weighting with instance weighting into one uniform framework. In AIWNB, the attribute weights are incorporated into the naive Bayesian classification formula, and then the prior and conditional probabilities are estimated using instance weighted training data. To learn instance weights, we single out an eager approach and a lazy approach, and thus two different versions are created, which we denote as AIWNBE and AIWNBL, respectively. Extensive experimental results show that both AIWNBE and AIWNBL significantly outperform NB and all the other existing state-of-the-art competitors. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Naive Bayes
Attribute weighting
Instance weighting
Eager learning
Lazy learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
A Shifting Trend Towards Endovascular Intervention in the Treatment of Acute Mesenteric Ischemia.png)
Cureus
OAAI
CureusIF0
OAAI分享收藏分享收藏
查看更多内容