Hierarchical Multi-label Classification using Fully Associative Ensemble Learning
作者：
Highlights：
• Developing a local hierarchical ensemble framework for Hierarchical Multi-label Classification (HMC), in which all the structural relationships in the class hierarchy are used to obtain global prediction.
• Introducing empirical loss minimization into HMC, so that the learned model can capture the most useful information from historical data.
• Proposing sparse, kernel, and binary constraint HMC models.
摘要
•Developing a local hierarchical ensemble framework for Hierarchical Multi-label Classification (HMC), in which all the structural relationships in the class hierarchy are used to obtain global prediction.•Introducing empirical loss minimization into HMC, so that the learned model can capture the most useful information from historical data.•Proposing sparse, kernel, and binary constraint HMC models.
论文关键词：Hierarchical multi-label classification,Ensemble learning,Ridge regression
论文评审过程：Received 26 October 2016, Revised 4 April 2017, Accepted 7 May 2017, Available online 8 May 2017, Version of Record 15 May 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.05.007
