Simpl_eClass: Simplified potential-free evolving fuzzy rule-based classifiers
Simpl_eClass: Simplified potential-free evolving fuzzy rule-based classifiers
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Simpl_eClass：简化的无势演化模糊基于规则的分类器
DOI：
10.1109/icsmc.2011.6084013
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
J. Andreu
中科院分区：
文献类型：
--
作者：
R. Baruah;P. Angelov;J. Andreu
本文提出了进化模糊规则分类器eClass的续集，这里称为简化的进化分类器，eClass。与eClass类似，Simple_eClass由两个不同的分类器组成，即零阶和一阶（simple_eClass0和simple_eClass1）。这两个分类器的模糊规则的后果部分，以及所使用的分类策略彼此不同。该设计基于密度增量原则，最近在所谓的EST-eTS+方法中引入。在eClass_eClass中的规则学习不涉及潜在值的计算，这使得它能够获得比eClass更便宜的计算模型更新阶段。与其他FRB分类器相比，它保留了eClass的所有优点，例如在线和进化，具有零阶和一阶。与其他非模糊分类器相比，它具有可解释性和透明性（特别是零阶分类器）的优点。本文的目的是证明的适用性的EQUIPMENT_eTS+的分类任务，并实证表明，简化的eClass到EQUIPMENT_eClass通过使用电位自由的方法不妥协的分类器的准确性。为了达到目标，分类器进行了测试，使用基准数据集进行了几个实验。simple_eClass1分类器也适用于现实生活中的问题，在线场景分类的低资源设备受益于其低计算成本。实验结果表明，该算法在简化规则学习过程的同时，达到了eClass的准确率。
This paper presents the sequel of evolving fuzzy rule-based classifier eClass, called here as simplified evolving classifier, simpl_eClass. Similarly to eClass, simpl_eClass comprises of two different classifiers, namely zero and first order (simpl_eClass0 and simpl_eClass1). The two classifiers differ from each other in terms of the consequent part of the fuzzy rules, and the classification strategy used. The design of simpl_eClass is based on the density increment principle introduced recently in so called simpl_eTS+ approach. The rule learning in simpl_eClass does not involve computation of potential values that allows it to attain computationally much less expensive model update phase compared to eClass. As compared to other FRB classifiers, it retains all the advantages of eClass, such as being on-line and evolving, having zero and first order. In comparison with other non-fuzzy classifiers it has the advantage of interpretability and transparency (especially zero order type). The goals of this paper are to demonstrate the applicability of simpl_eTS+ to classification task, and to empirically show that the simplification of eClass to simpl_eClass by using potential-free approach does not compromise the accuracy of the classifiers. In order to attain the goals, the classifiers are tested by performing several experiments using benchmark data sets. The simpl_eClass1 classifier is also applied to the real-life problem of on-line scene categorization for low-resource devices benefiting from its low computational cost. The results obtained from the experiments endorse that simpl_eClass achieves the accuracy of eClass while simplifying rule learning process.
影响因子：
11.9
作者：
Angelov, Plamen P.;Zhou, Xiaowei
通讯作者：
Zhou, Xiaowei