Evolving Fuzzy-Rule-Based Classifiers From Data Streams
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
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DOI：
10.1109/tfuzz.2008.925904
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发表时间：
2008-12-01
影响因子：
11.9
通讯作者：
Zhou, Xiaowei
中科院分区：
文献类型：
--
作者：
Angelov, Plamen P.;Zhou, Xiaowei
介绍了一种流数据在线分类的新方法。它是基于自开发（进化）的基于模糊规则（FRB）的Takagi-Sugeno （eTS）型分类器系统。提出的方法称为eClass（进化分类器），包括不同的体系结构和在线学习方法。备选架构家族包括：1)eClass0，分类器结果表示类标签；2)新提出的使用一阶eTS模糊分类器对特征进行回归的方法，eClass1。eClass的一个重要特性是它可以“从零开始”学习。不仅模糊规则不需要预先指定，而且eClass的类的数量也不需要预先指定（随着在线学习过程中添加新的类标签，类的数量可能会增加）。在初始FRB存在的情况下，eClass可以根据新到达的数据进一步发展/开发它。提出的方法解决了流数据分类的实际问题（视频、语音、机器人产生的感官数据、先进的工业应用、金融和零售链交易、入侵者检测等）。该方法已成功地在多个基准问题上进行了测试，并对入侵检测数据流中的数据进行了测试，以与已建立的方法进行比较。结果表明，利用有限的计算资源，可以从流数据在线生成灵活的FRB分类器，实现高分类率。
A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (evolving) fuzzy-rule-based (FRB) classifier system of Takagi-Sugeno (eTS) type. The proposed approach, called eClass (evolving classifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1) eClass0, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order eTS fuzzy classifier, eClass1. An important property of eClass is that it can start learning "from scratch." Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for eClass (the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists, eClass can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources.