Self-Fuzzification Method according to Typicality Correlation for Classification on tiny Data Sets

E. Schmitt, V. Bombardier, P. Charpentier
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引用次数: 10

Abstract

This article presents a self-fuzzification method to enhance the settings of a fuzzy reasoning classification adapted to the automated inspection of wooden boards. The supervised classification is made thanks to fuzzy linguistic rules generated from small training data sets. This study especially answers to a double industrial need about the pattern recognition in wooden boards. Firstly, few samples are available to generate the recognition model. This aspect makes lesser efficient compilation methods like neural networks in terms of recognition rates. Secondly, the settings of the classification method must be simplified, because the users are not experts in fuzzy logic. In this article, two points are presented. The first part demonstrates the generalization capability of the presented classification method in comparison to more classical algorithms. In the second part, we propose a new automatic method of parameter fuzzification, by using the typicality correlation coefficients of each class.
基于典型关联的自模糊化小数据集分类方法
本文提出了一种自模糊化方法,以增强适合于木板自动检测的模糊推理分类的设置。监督分类是由小型训练数据集生成的模糊语言规则实现的。本研究特别满足了工业对木板图案识别的双重需求。首先,用于生成识别模型的样本很少。这方面使得像神经网络这样的编译方法在识别率方面效率较低。其次,分类方法的设置必须简化,因为用户不是模糊逻辑专家。在本文中,提出了两点。第一部分演示了所提出的分类方法与更经典算法的泛化能力。在第二部分,我们提出了一种新的参数模糊化自动方法,利用每一类的典型相关系数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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