文章基本信息

标题：frbs: Fuzzy Rule-Based Systems for Classification and Regression in R
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作者：Lala Septem Riza ; Christoph Bergmeir ; Francisco Herrera 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2015
卷号：65
期号：1
页码：1-30
语种：English
出版社：University of California, Los Angeles
摘要：Fuzzy rule-based systems (FRBSs) are a well-known method family within soft computing. They are based on fuzzy concepts to address complex real-world problems. We present the R package frbs which implements the most widely used FRBS models, namely, Mamdani and Takagi Sugeno Kang (TSK) ones, as well as some common variants. In addition a host of learning methods for FRBSs, where the models are constructed from data, are implemented. In this way, accurate and interpretable systems can be built for data analysis and modeling tasks. In this paper, we also provide some examples on the usage of the package and a comparison with other common classification and regression methods available in R.