A novel random forest approach for imbalance problem in crime linkage

作者：

Highlights：

• An information granule is employed to find indistinguishable crime pairs.

• A novel random forest (IGRF) is proposed to solve the class imbalance.

• The classification performance of proposed approach is evaluated on a real-world robbery dataset.

• The proposed approach is combined with other methods solving class imbalance.

摘要

•An information granule is employed to find indistinguishable crime pairs.•A novel random forest (IGRF) is proposed to solve the class imbalance.•The classification performance of proposed approach is evaluated on a real-world robbery dataset.•The proposed approach is combined with other methods solving class imbalance.

论文关键词：Crime linkage,Classification,Class imbalance,Information granule,Random forest

论文评审过程：Received 30 September 2019, Revised 1 March 2020, Accepted 4 March 2020, Available online 9 March 2020, Version of Record 4 April 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.105738