Fuzzy granular classification based on the principle of justifiable granularity
Fuzzy granular classification based on the principle of justifiable granularity
复制标题
基于合理粒度原则的模糊粒度分类
DOI：
10.1016/j.knosys.2019.02.001
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发表时间：
2019-04
影响因子：
8.8
通讯作者：
Jianhua Yang
中科院分区：
文献类型：
--
作者：
Chen Fu;Wei Lu;Witold Pedrycz;Jianhua Yang
基于模糊规则的分类器由于其简单的模块化结构和显著的可解释性而在许多应用中表现出更好的性能。一般而言，研究者们沿着两个互不相容的方向设计这类分类器：一个致力于从数据中挖掘更多的规则以提高分类器的准确率，另一个致力于减少规则的数量以简化分类器并提高分类器的准确率。然而，从实用性的角度来看，在设计分类器时，应该仔细考虑模型的准确性和模型中包含的规则数量之间的权衡。此外，这些分类器在设计过程中也遇到了两个明显的限制：一个是每个样本与单个属性的贡献是同等重要的，另一个是没有考虑信息粒度的概念。这两个限制导致随后的分类器的准确性降低。为了缓解这两个局限性，并在准确性和形成的分类器的规则数量之间做出妥协，在这项研究中，提出了一种新的方法来构建一个模糊分类器的合理粒度的原则与加权数据。所提出的方法包括两个主要阶段：第一阶段涉及的初始分类模型的形成。初始模型是通过模糊C均值聚类（FCM）和合理的粒度与加权数据的原则的协同作用。第二阶段侧重于初始分类模型的细化。完全形成的模糊分类模型的规则数等于实验数据的类别数。通过对人工数据集和10个UCI数据集的实验，验证了该方法的可行性和有效性，并揭示了信息粒度对分类模型的影响。
Fuzzy rule-based classifiers have been used in many applications showing better performance due to simple modular architectures and significant interpretability. Generally, researchers design this type of classifiers along with two incompatible directions: some of them devote to mine more rules from data for enhancing the accuracy of classifiers and others focus on reducing the number of rules for simplifying the classifier with high accuracy. However, from the perspective of practicability, the tradeoff between the accuracy of model and the number of rules including in the model should be carefully considered when designing classifiers. Further, these classifiers also encounter two evident limitations in the process of design: one is that the contribution of each sample versus individual attributions is equally important and another is that the concept of information granularity is not considered. These two limitations result in the reduction of accuracy of the ensuing classifier. To alleviate these two limitations and make compromise between the accuracy and the number of rules of formed classifier, in this study, a novel method is proposed to construct a fuzzy classifier by means of the principle of justifiable granularity with weighted data. The proposed method involves two main stages: the first stage concerns with the formation of an initial classification model. The initial model is constructed by engaging a synergy of Fuzzy C-Means clustering (FCM) and the principle of justifiable granularity with weighted data. The second stage focuses on the refinement of the initial classification model. The number of rules of the completely formed fuzzy classification model is equal to that of classes of experimental data. A series of experiments concerning synthetic datasets and ten UCI datasets are also implemented to exhibit the feasibility and effectiveness of the proposed classification method as well as reveal the impact of information granularity on resulting classification model.
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DOI：
10.1109/fuzz-ieee.2016.7737793
发表时间：
2016-07
期刊：
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
影响因子：
--
作者：
Xiubin Zhu;W. Pedrycz;Zhiwu Li
通讯作者：
Xiubin Zhu;W. Pedrycz;Zhiwu Li
DOI：
10.1109/nafips.2008.4531354
发表时间：
2008-05
期刊：
--
影响因子：
--
作者：
L. Zadeh
通讯作者：
L. Zadeh
DOI：
10.1007/978-1-4471-0285-4
发表时间：
2012-10
期刊：
--
影响因子：
--
作者：
Shigeo Abe DrEng
通讯作者：
Shigeo Abe DrEng
影响因子：
3.9
作者：
Nauck, D;Kruse, R
通讯作者：
Kruse, R
DOI：
10.1016/j.asoc.2016.10.029
发表时间：
2017-03
期刊：
Appl. Soft Comput.
影响因子：
--
作者：
Andrii Shalaginov;K. Franke
通讯作者：
Andrii Shalaginov;K. Franke