Fuzzy vector quantization with a step-optimizer to improve pattern classification
作者：
Highlights：
• A novel pattern classifier algorithm using Fuzzy Vector Quantization is proposed.
• Fuzzy-space representation of data eliminates the problem of class-overlapping.
• Introducing optimization to determine the step size of the quantization process.
• Validation of the classifier algorithm on various high dimensional benchmark datasets.
• Achieved higher accuracy with significantly low computation cost.
摘要
•A novel pattern classifier algorithm using Fuzzy Vector Quantization is proposed.•Fuzzy-space representation of data eliminates the problem of class-overlapping.•Introducing optimization to determine the step size of the quantization process.•Validation of the classifier algorithm on various high dimensional benchmark datasets.•Achieved higher accuracy with significantly low computation cost.
论文关键词：Fuzzy logic,Vector quantization,Step size optimization,Uniform manifold approximation and projection,UMAP,Classification algorithm,MNIST,BCI,Linear and non-linear classification,Multi-class high dimensional features
论文评审过程：Received 11 December 2020, Revised 25 August 2021, Accepted 18 September 2021, Available online 30 September 2021, Version of Record 19 October 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115941
