A design framework for hierarchical ensemble of multiple feature extractors and multiple classifiers
作者：
Highlights：
• Optimization of MFMC (multiple feature-extractor, multiple classifier) systems.
• Presentation of a general design framework for an ensemble of MFMC.
• Proposing a hierarchical approach for reducing the complexity of MFMC optimization.
• Proposing a new approach that integrates reinforcement learning and Bayesian network.
• Experimental results show that the proposed framework outperforms previous approaches.
摘要
•Optimization of MFMC (multiple feature-extractor, multiple classifier) systems.•Presentation of a general design framework for an ensemble of MFMC.•Proposing a hierarchical approach for reducing the complexity of MFMC optimization.•Proposing a new approach that integrates reinforcement learning and Bayesian network.•Experimental results show that the proposed framework outperforms previous approaches.
论文关键词：Ensemble of detection systems,Multiple feature extractors,Multiple classifiers,Pedestrian detection,Reinforcement learning,Bayesian network
论文评审过程：Received 2 May 2014, Revised 17 August 2015, Accepted 10 November 2015, Available online 30 November 2015, Version of Record 24 December 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.11.006
