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المؤلفShi, Qiushi
المؤلفHu, Minghui
المؤلفSuganthan, Ponnuthurai Nagaratnam
المؤلفKatuwal, Rakesh
تاريخ الإتاحة2023-02-09T07:06:16Z
تاريخ النشر2022-12-01
اسم المنشورPattern Recognition
المعرّفhttp://dx.doi.org/10.1016/j.patcog.2022.108879
الاقتباسShi, 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.‏
الرقم المعياري الدولي للكتاب00313203
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135340847&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/39872
الملخصIn 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.
اللغةen
الناشرElsevier Ltd
الموضوعEnsemble deep random vector functional link (edRVFL)
Pruning
UCI classification datasets
Weighting methods
العنوانWeighting and pruning based ensemble deep random vector functional link network for tabular data classification
النوعArticle
رقم المجلد132
dc.accessType Open Access


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