Fast incremental learning of logistic model tree using least angle regression

作者：

Highlights：

• Computational efficiency of logistic model tree (LMT) algorithm is improved.

• An efficient boosting method for sparse logistic regression learning is proposed.

• The proposed method employs least angle regression to incorporate variable selection into the boosting process.

• Experimental results on 14 datasets to compare the proposed method with the original LMT algorithm are presented.

摘要

•Computational efficiency of logistic model tree (LMT) algorithm is improved.•An efficient boosting method for sparse logistic regression learning is proposed.•The proposed method employs least angle regression to incorporate variable selection into the boosting process.•Experimental results on 14 datasets to compare the proposed method with the original LMT algorithm are presented.

论文关键词：Model trees,Decision trees,Logistic regression,Boosting,Least angle regression,Classification

论文评审过程：Received 29 May 2017, Revised 6 December 2017, Accepted 7 December 2017, Available online 7 December 2017, Version of Record 21 December 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.12.014