A dynamic time warping approach for handling class imbalanced medical datasets with missing values: A case study of protein localization site prediction

作者：

Highlights：

• This paper focuses on missing value imputation in class imbalanced medical datasets.

• Dynamic Time Warping-Based Imputation (DTWI) is proposed.

• All of the data with or without missing values in the minority class can be used for imputation.

• DTWI significantly outperform the other techniques when the missing rates are larger than 30%.

摘要

•This paper focuses on missing value imputation in class imbalanced medical datasets.•Dynamic Time Warping-Based Imputation (DTWI) is proposed.•All of the data with or without missing values in the minority class can be used for imputation.•DTWI significantly outperform the other techniques when the missing rates are larger than 30%.

论文关键词：Class imbalance,Data mining,Dynamic time warping,Machine learning,Missing value imputation

论文评审过程：Received 1 May 2021, Revised 16 October 2021, Accepted 19 December 2021, Available online 23 December 2021, Version of Record 27 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116437