Weighting and pruning based ensemble deep random vector functional link network for tabular data classification. (December 2022)

• Record Type: Journal Article
• Authors: Shi, Qiushi; Hu, Minghui; Suganthan, Ponnuthurai Nagaratnam; Katuwal, Rakesh
• Is Part Of: Pattern recognition. Volume 132(2022)
• Publication Date: 2022-12
• Subjects: Ensemble deep random vector functional link (edRVFL) -- Weighting methods -- Pruning -- UCI classification datasets; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108879
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: In this paper, we first integrate normalization to the Ensemble Deep Random Vector Functional Link network (edRVFL). This re-normalization step can help the network avoid divergence of the hidden features. Then, we propose novel variants of the edRVFL network. Weighted edRVFL (WedRVFL) uses weighting methods to give training samples different weights in different layers according to how the samples were classified confidently in the previous layer thereby increasing the ensemble's diversity and accuracy. Furthermore, a pruning-based edRVFL (PedRVFL) has also been proposed. We prune some inferior neurons based on their importance for classification before generating the next hidden layer. Through this method, we ensure that the randomly generated inferior features will not propagate to deeper layers. Subsequently, the combination of weighting and pruning, called Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network (WPedRVFL), is proposed. We compare their performances with other state-of-the-art classification methods on 24 tabular UCI classification datasets. The experimental results illustrate the superior performance of our proposed methods.