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Type-2 AFS classification method based on sample selection(PDF)


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Type-2 AFS classification method based on sample selection
Liu Yifei1Guo Hongyue2Liu Xiaodong1
1.College of Science,Dalian Maritime University,Dalian 116026,China; 2.School of Maritime Economics and Management,Dalian Maritime University,Dalian 116026,China
sample selection unstable cut-points threshold classifier
The semantic description is a hotspot in the field of clustering and classification. The classification algorithm based on axiomatic fuzzy sets(AFS)can imitate the mechanism of human reasoning to obtain the class description with sound interpretability and practicality. A concise class descriptions will help us to better understand classification results under the same classification performance. This study employs the sample selection algorithm of unstable cut points(UCSS)and interval type-2 membership function to design the type-2 AFS classification algorithm based on the sample selection of unstable cut points(UCSS-AFS). The classification method can effectively reduce the class description complexity as well as maintaining good classification accuracy and semantics. To illustrate the practicability of the designed method,this study conducts experiments on 18 data sets selected from the UCI database. Experimental results exhibit that the rules derived from the UCSS-AFS classification method are easy to understand and solve real problem with a sound classification accuracy.


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Last Update: 2019-09-30