A new probabilistic classifier based on decomposable models with application to internet traffic
作者：
Highlights：
• In this paper, we proposed a new algorithm called LDMLCS to build a family of decomposable models that are appropriate for classification process.
• LDMLCS can generate numerous decomposable models, including simple models such as Tree Augmented Naive Bayes (TAN) and complex models with large joint marginal distribution, and some models that could be used for classification on a given dataset.
• This algorithm can address the problem of over-fitting and also capture the interaction between different features effectively and consequently, the obtained model is easily interpretable.
摘要
•In this paper, we proposed a new algorithm called LDMLCS to build a family of decomposable models that are appropriate for classification process.•LDMLCS can generate numerous decomposable models, including simple models such as Tree Augmented Naive Bayes (TAN) and complex models with large joint marginal distribution, and some models that could be used for classification on a given dataset.•This algorithm can address the problem of over-fitting and also capture the interaction between different features effectively and consequently, the obtained model is easily interpretable.
论文关键词：Decomposable models,Over-fitting,TAN based on Chow–Liu algorithm,Averaged TAN,t-Cherry algorithm,Internet traffic classification
论文评审过程：Received 5 May 2017, Revised 17 November 2017, Accepted 9 December 2017, Available online 11 December 2017, Version of Record 19 December 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.12.009
