FuzzyCART:一种基于模糊逻辑的分类与回归树算法

Piers R. J. Campbell, H. Fathulla, Faheem Ahmed
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引用次数: 0

摘要

分类算法在许多领域都有很高的应用。分类回归树(classification And Regression Trees, CART)是目前广泛使用的一种重要的分类算法,它能在大多数多领域产生准确一致的结果。CART和其他类似算法的一个重大缺陷是它们无法处理不精确。由于无法处理“灰色地带”,这些算法不太适用于医学和金融等一系列领域。模糊逻辑是一种很好的处理这种不精确的方法,本文提出了一种结合CART和模糊逻辑的新算法。在描述实现之后,通过使用所提出的FuzzyCART算法实现的实验结果表明,与经典CART相比,医疗数据的分类精度提高了。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FuzzyCART: A novel Fuzzy Logic based Classification & Regression Trees Algorithm
Classification algorithms have found high levels of application in a range of domains. One of the most important classification algorithms that is currently in wide use Classification And Regression Trees (CART), which yields accurate and consistent results in most multiple domains. A significant failing of CART and other similar algorithms is their inability to handle imprecision. This inability to handle the “grey areas” makes these algorithms less applicable to a range of domains such as Medicine and Finance. A well-regarded method for handling such imprecision is Fuzzy Logic, and in this paper a novel algorithm that combines CART and Fuzzy Logic is presented. Following the description of the implementation the experimental results presented which have been achieved through the use of the proposed FuzzyCART algorithm demonstrate an increased level of classification accuracy for medical data when compared to classical CART.
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