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Axiomatic Fuzzy Set Theory and Its Applications pp 423–489Cite as

AFS Fuzzy Classifiers

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 244))

Abstract

In this chapter, we introduce three design strategies of classifiers which exploit the unified usage of the AFS fuzzy logic, entropy measures and decision trees. The advantage of these classifiers is two-fold. First, they can mimic the human reasoning and in this manner offer a far more transparent and comprehensible way supporting the design of the classifiers. An important aspect is concerned with the simplicity of the design methodology and the clarity of the underlying semantics. We use three well known data to illustrate the effectiveness of the classifiers and present the relationship between the parameters of the classifiers and their performance.

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  1. Xiaodong Liu
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Liu, X., Pedrycz, W. (2009). AFS Fuzzy Classifiers. In: Axiomatic Fuzzy Set Theory and Its Applications. Studies in Fuzziness and Soft Computing, vol 244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00402-5_10

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