Chen Long, Chen C L Philip, Pedrycz Witold
Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249-0669, USA.
IEEE Trans Syst Man Cybern B Cybern. 2010 Oct;40(5):1219-30. doi: 10.1109/TSMCB.2009.2036443. Epub 2009 Dec 4.
Linguistic interface is a group of linguistic terms or fuzzy descriptions that describe variables in a system utilizing corresponding membership functions. Its transparency completely or partly decides the interpretability of fuzzy models. This paper proposes a GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach to overcome the disadvantage of traditional linguistic interface generation methods where the consideration of the interpretability aspects of linguistic interface is limited. In GREETING, the widely used interpretability criteria of linguistic interface are considered and optimized. The numeric experiments on the data sets from University of California, Irvine (UCI) machine learning databases demonstrate the feasibility and superiority of the proposed GREETING method. The GREETING method is also applied to fuzzy decision tree generation. It is shown that GREETING generates better transparent fuzzy decision trees in terms of better classification rates and comparable tree sizes.
语言接口是一组利用相应隶属函数描述系统中变量的语言术语或模糊描述。其透明度完全或部分决定了模糊模型的可解释性。本文提出了一种基于梯度下降的透明语言接口生成（GREETING）方法，以克服传统语言接口生成方法在语言接口可解释性方面考虑有限的缺点。在GREETING中，考虑并优化了广泛使用的语言接口可解释性标准。在加利福尼亚大学欧文分校（UCI）机器学习数据库的数据集上进行的数值实验证明了所提出的GREETING方法的可行性和优越性。GREETING方法也应用于模糊决策树生成。结果表明，GREETING在更好的分类率和可比的树规模方面生成了更好的透明模糊决策树。
