Adjusted weight voting algorithm for random forests in handling missing values

作者：

Highlights：

• A novel algorithm based on random forests with surrogate splits is proposed to address the classification problem of incomplete data without imputation.

• The algorithm allows each tree to cast a vote even the voting process is interrupted by missing attributes.

• Experimental results on various acknowledged datasets show that the proposed method is robust and efficient.

摘要

•A novel algorithm based on random forests with surrogate splits is proposed to address the classification problem of incomplete data without imputation.•The algorithm allows each tree to cast a vote even the voting process is interrupted by missing attributes.•Experimental results on various acknowledged datasets show that the proposed method is robust and efficient.

论文关键词：Random forests,Missing values,Imputation approaches,Surrogate decisions,Weighted voting

论文评审过程：Received 19 July 2016, Revised 11 March 2017, Accepted 4 April 2017, Available online 5 April 2017, Version of Record 13 April 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.04.005