A weighted distance-based approach with boosted decision trees for label ranking

作者：

Highlights：

• Defining an item-weighted Label Ranking algorithm.

• Mapping from instances to rankings over a finite set of predefined labels.

• Improving the predictive performance by aggregating many decision trees.

• Interpretative method to measure the overall covariates’ importance.

摘要

•Defining an item-weighted Label Ranking algorithm.•Mapping from instances to rankings over a finite set of predefined labels.•Improving the predictive performance by aggregating many decision trees.•Interpretative method to measure the overall covariates’ importance.

论文关键词：Label ranking,Boosting,Weighted ranking data,Ensemble methods,Decision trees

论文评审过程：Received 27 December 2021, Revised 19 July 2022, Accepted 7 October 2022, Available online 14 October 2022, Version of Record 21 October 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.119000