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Title: Weighting and pruning based ensemble deep random vector functional link network for tabular data classification
Authors: Shi, Qiushi
Hu, Minghui
Suganthan, Ponnuthurai Nagaratnam
Katuwal, Rakesh
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Shi, Q., Hu, M., Suganthan, P. N. & Katuwal, R. (2022). Weighting and pruning based ensemble deep random vector functional link network for tabular data classification. Pattern Recognition, 132, 108879-.
Journal: Pattern Recognition
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.
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2022.108879
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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