Fuzzy classifier based on clustering with pairs of ε-hyperballs and its application to support fetal state assessment

作者：

Highlights：

• Pairs of ε-hyperballs improve the effectiveness of fuzzy classification.

• Clustering with pairs of prototypes efficiently supports automated fetal state assessment.

• Two-step cardiotocogram analysis increases sensitivity of fetal diagnosis.

摘要

•Pairs of ε-hyperballs improve the effectiveness of fuzzy classification.•Clustering with pairs of prototypes efficiently supports automated fetal state assessment.•Two-step cardiotocogram analysis increases sensitivity of fetal diagnosis.

论文关键词：Fuzzy classifier,Fuzzy rule extraction,Fuzzy clustering,ε-Insensitivity,Fetal monitoring

论文评审过程：Received 7 March 2018, Revised 4 September 2018, Accepted 12 September 2018, Available online 13 September 2018, Version of Record 10 October 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.030