Multi-kernel multi-criteria optimization classifier with fuzzification and penalty factors for predicting biological activity
作者：
Highlights：
• A novel MK–MCOC–FP approach is proposed for predicting active compounds in bioassay.
• Multi-kernel method reduces dimensionality and gains the interpretable classifier.
• A fuzzification method using class median minimizes the effect of outliers.
• The class-imbalanced penalty factors tune overfitting and underfitting.
• Our proposed classifier obtains better performance in flexibility and accuracy.
摘要
•A novel MK–MCOC–FP approach is proposed for predicting active compounds in bioassay.•Multi-kernel method reduces dimensionality and gains the interpretable classifier.•A fuzzification method using class median minimizes the effect of outliers.•The class-imbalanced penalty factors tune overfitting and underfitting.•Our proposed classifier obtains better performance in flexibility and accuracy.
论文关键词：Multi-kernel learning,Multi-criteria optimization,Fuzzification,Class-imbalanced learning,Classification,Bioassay
论文评审过程：Received 14 April 2015, Revised 12 July 2015, Accepted 13 July 2015, Available online 17 July 2015, Version of Record 19 October 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.07.011
