Weighting and pruning based ensemble deep random vector functional link network for tabular data classification
作者：
Highlights：
• Three variants of Ensemble Deep Random Vector Functional Link network are proposed.
• Batch normalization is introduced to the edRVFL network to re-normalize the hidden features.
• Weighting and pruning methods are employed to improve the classification ability.
• No re-training of previous layers is required for these networks when adding one more hidden layer.
• Our best method outperforms other 13 classifiers on 24 datasets.
摘要
•Three variants of Ensemble Deep Random Vector Functional Link network are proposed.•Batch normalization is introduced to the edRVFL network to re-normalize the hidden features.•Weighting and pruning methods are employed to improve the classification ability.•No re-training of previous layers is required for these networks when adding one more hidden layer.•Our best method outperforms other 13 classifiers on 24 datasets.
论文关键词：Ensemble deep random vector functional link (edRVFL),Weighting methods,Pruning,UCI classification datasets
论文评审过程：Received 23 February 2022, Revised 28 April 2022, Accepted 26 June 2022, Available online 27 June 2022, Version of Record 1 August 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108879
