A Granular Classifier By Means of Context-based Similarity Clustering
A Granular Classifier By Means of Context-based Similarity Clustering
复制标题
基于上下文的相似性聚类的粒度分类器
DOI：
10.5370/jeet.2016.11.5.1383
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发表时间：
2016
影响因子：
1.9
通讯作者：
Jiping Liao
中科院分区：
文献类型：
--
作者：
黄玮;Jinsong Wang;Jiping Liao
在本研究中，我们借助基于上下文的相似性聚类（CSC）方法提出了一种粒度分类器（GC），并将其应用于网络入侵检测。这里利用所提出的支持信息颗粒设计的 CSC 来确定所谓的上下文。与传统的相似聚类方法不同，这里 CSC 通过考虑输入数据和输出数据来构建聚类。粒度分类器的设计是基于if-then规则实现的，它由两部分组成：即前提部分和结论部分。前提部分是使用CSC开发的，结论部分是借助支持向量机实现的。与典型的基于规则的分类器相比，这里利用的基本原则是考虑充分利用输出数据的鲁棒分类。特别是，基于规则的分类器或支持向量机可以被视为所提出的粒度分类器的特例。数值研究表明了所提出方法的优越性。
In this study, we propose a granular classifier (GC) with the aid of a context-based similarity clustering (CSC) method and applied it for network intrusion detection. The proposed CSC supporting the design of information granules is exploited here to determine the so-called contexts. Unlike the conventional similar clustering method, here the CSC built clusters by taking into consideration of both input data and output data. The design of granular classifier is realized based on the if-then rules, which consists two parts: namely premise part and conclusion part. The premise part is developed by using the CSC, while the conclusion part is realized with the aid of supported vector machines. In contrast to typical rule-based classifier, the underlying principle exploited here is to consider a robust classification with the adequate use of output data. In particular, rule-based classifiers or supported vector machines can be regarded as a special case of the proposed granular classifier. Numeric studies show the superiority of the proposed approach.