Dependent binary relevance models for multi-label classification
作者：
Highlights：
• We propose DBR as a multi-label classifier that exploits conditional label dependence.
• DBR combines properties of both, chaining and stacking learning strategies.
• We provide a careful analysis of the relationship between these techniques.
• We study the underlying dependency structure and the type of training data used.
• Our experiments show the good performance of DBR in terms of several measures.
摘要
Highlights•We propose DBR as a multi-label classifier that exploits conditional label dependence.•DBR combines properties of both, chaining and stacking learning strategies.•We provide a careful analysis of the relationship between these techniques.•We study the underlying dependency structure and the type of training data used.•Our experiments show the good performance of DBR in terms of several measures.
论文关键词：Multi-label classification,Label dependence,Stacking,Chaining
论文评审过程：Received 31 May 2013, Revised 24 September 2013, Accepted 25 September 2013, Available online 4 October 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.09.029
