A novel hierarchical selective ensemble classifier with bioinformatics application
作者：
Highlights：
• Feature selection is based on parallel optimization and hierarchical selection.
• Maximize the sum of relevance and distance solves problem of high dimensionality.
• A multi-class problem can be transformed into a binary classification problem.
• Ensemble combination method based on divide-and-conquer strategy is efficient.
• Solving bioinformatics problems with high-level performance can be achieved.
摘要
•Feature selection is based on parallel optimization and hierarchical selection.•Maximize the sum of relevance and distance solves problem of high dimensionality.•A multi-class problem can be transformed into a binary classification problem.•Ensemble combination method based on divide-and-conquer strategy is efficient.•Solving bioinformatics problems with high-level performance can be achieved.
论文关键词：Selective ensemble learning,Parallel optimization,Divide and conquer,Multi-class classification,Bioinformatics
论文评审过程：Received 23 December 2016, Revised 9 February 2017, Accepted 10 February 2017, Available online 27 February 2017, Version of Record 17 November 2017.
论文官网地址：https://doi.org/10.1016/j.artmed.2017.02.005
