Fuzzy inference system & fuzzy cognitive maps based classification

Kanika Bhutani, Gaurav, Megha Kumar
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引用次数: 4

Abstract

Fuzzy classification is very necessary because it has the ability to use interpretable rules. It has got control over the limitations of crisp rule based classifiers. This paper mainly deals with classification on the basis of soft computing techniques fuzzy cognitive maps and fuzzy inference system on the lenses dataset. The results obtained with FIS shows 100% accuracy. Sometimes the data available for classification contain missing or ambiguous data so Neutrosophic logic is used for classification to deal with indeterminacy.
模糊推理系统&基于模糊认知地图的分类
模糊分类是非常必要的,因为它具有使用可解释规则的能力。它控制了基于规则的分类器的局限性。本文主要研究了基于软计算技术的模糊认知图和模糊推理系统在镜头数据集上的分类。结果表明,FIS的准确度为100%。有时可用于分类的数据包含缺失或模糊的数据,因此使用中性逻辑进行分类以处理不确定性。
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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