Naive Bayes Associative Classification of Mammographic Data

摘要：

In this paper we focus on a new model, named ANB (Associative Naive Bayes) model. ANB model extend the modeling flexibility of well known Naive Bayes (NB) models by introducing rules generated by associative classifier. The model consists of two layers: an input layer and an internal layer. We propose an associative classifier algorithm (AAC), relaxing the condition of independence of attributes in NB, for generating rules and learning network parameter and a simple algorithm for training ANB models in the context of classification. Experimental results show that the learned models can significantly improve classification accuracy as compared to NB.

关键词： Naive Bayes classifier association rule associative classification Bayes theorem

作者: Benaki Lairenjam Siri krishan Wasan

作者单位: Department of Mathematics, Jamia Millia Islamia New Delhi, India

会议类型: 国际会议

会议名称: 2010 International Conference on Educational and Network Technology(2010教育与网络技术国际会议 ICENT 2010)

会议地点: 秦皇岛

会议语种:英文

页码: 276-281

在线出版日期: 2010-06-25（万方平台首次上网日期，不代表论文的发表时间）