Learning from multi-label data with interactivity constraints: An extensive experimental study
作者：
Highlights：
• Extensive study of 12 multi-label learning methods with interactivity constraints.
• Focus on the beginning of the classification task where few examples are available.
• Experimental evaluation with a protocol independent of any implementation environment.
• Classifier performances are evaluated for 7 quality and time criteria on 12 datasets.
• RF-PCT obtains the best predictive performance while being computationally efficient.
摘要
•Extensive study of 12 multi-label learning methods with interactivity constraints.•Focus on the beginning of the classification task where few examples are available.•Experimental evaluation with a protocol independent of any implementation environment.•Classifier performances are evaluated for 7 quality and time criteria on 12 datasets.•RF-PCT obtains the best predictive performance while being computationally efficient.
论文关键词：Interactive learning,Multi-label learning,Comparative study
论文评审过程：Available online 19 March 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.03.006
