An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis
作者：
Highlights：
• This study proposes an improved MDR using the fuzzy sigmoid method (FSMDR) to detect epistasis.
• The 46 epistatic models and a large real data set are used to compare FSMDR with other MDR-based methods.
• The results demonstrate that FSMDR achieves superior detection rates than other MDR-based methods on epistasis detection.
• The fuzzy sigmoid method effectively improves the discrimination of two multifactor genotypes in MDR operation.
• FSMDR does not need to select the optimal adjustment parameters of fuzzy sets in practical data applications.
摘要
•This study proposes an improved MDR using the fuzzy sigmoid method (FSMDR) to detect epistasis.•The 46 epistatic models and a large real data set are used to compare FSMDR with other MDR-based methods.•The results demonstrate that FSMDR achieves superior detection rates than other MDR-based methods on epistasis detection.•The fuzzy sigmoid method effectively improves the discrimination of two multifactor genotypes in MDR operation.•FSMDR does not need to select the optimal adjustment parameters of fuzzy sets in practical data applications.
论文关键词：Fuzzy set,Epistasis,Multifactor dimensionality reduction,Classification
论文评审过程：Received 5 December 2018, Revised 18 October 2019, Accepted 19 November 2019, Available online 22 November 2019, Version of Record 30 November 2019.
论文官网地址：https://doi.org/10.1016/j.artmed.2019.101768
