Evolving fuzzy classifiers using different model architectures
Evolving fuzzy classifiers using different model architectures
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DOI：
10.1016/j.fss.2008.06.019
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发表时间：
2008-12-01
影响因子：
3.9
通讯作者：
Zhou, X.
中科院分区：
文献类型：
--
作者：
Angelov, P.;Lughofer, E.;Zhou, X.
在本文中，我们提出了两种新的方法在线发展模糊分类器，称为eClass和FLEXFIS类。这两种方法可以应用于不同的模型架构，包括单模型（SM）与类标签作为结果，分类超平面作为结果，和多模型（MM）架构。此外，eClass可以具有多输入多输出（MIMO）架构，其中多个超平面作为每个模糊规则的结果。MM和MIMO架构之间的区别在于，前者为每个类应用一个单独且独立的基于模糊规则（FRB）的分类器，并使用指示符标签方案，而后者应用单个FRB，其中规则是MIMO而不是MISO。eClass和FLEXFIS-Class方法都设计为在每个样本的基础上工作，因此是一次通过的增量方法。此外，它们的结构（FRB）是不断发展的，而不是固定的。它会根据任何新加载的样本调整它们在前件和后件中的参数。一个特别强调的是放在先进的问题，以提高准确性和鲁棒性，包括一个彻底的比较全局和局部学习的后续功能，一种新的方法，用于检测和反应的漂移数据流和增强离群值处理策略。根据先进的问题的方法和他们的扩展进行评估的一个基准问题的手写图像识别，以及在现实生活中的问题的图像分类框架，其中图像应分类成好的和坏的在线和交互式的生产过程中。(C)2008 Elsevier B. V.保留所有权利。
In this paper we present two novel approaches for on-line evolving fuzzy classifiers, called eClass and FLEXFIS-Class. Both methods can be applied with different model architectures, including single model (SM) with class labels as consequents, classification hyper-planes as consequents, and multi-model (MM) architecture. Additionally, eClass can have a multi-input-multi-output (MIMO) architecture with multiple hyper-planes as consequents of each fuzzy rule. The difference between MM and MIMO architectures is that the former one applies one separate and independent fuzzy rule-based (FRB) classifier for each class and is using an indicator labelling scheme, while the latter one applies a single FRB where the rules are MIMO rather than MISO. Both, eClass and FLEXFIS-Class methods are designed to work on a per-sample basis and are thus one-pass, incremental. Additionally, their structure (FRB) is evolving rather than fixed. It adapts their parameters in antecedent and consequent parts with any newly loaded sample. A special emphasis is placed on advanced issues for improving accuracy and robustness, including a thorough comparison between global and local learning of consequent functions, a novel approach for detecting of and reacting on drifts in the data streams and an enhanced outlier treatment strategy. The methods and their extensions according to the advanced issues are evaluated on one benchmark problem of handwritten images recognition as well as on a real-life problem of image classification framework, where images should be classified into good and bad ones during an on-line and interactive production process. (C) 2008 Elsevier B.V. All rights reserved.