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Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification. (arXiv:2201.05809v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
In this paper, we first introduce batch normalization to the edRVFL network.
This re-normalization method can help the network avoid divergence of the
hidden features. Then we propose novel variants of Ensemble Deep Random Vector
Functional Link (edRVFL). 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 …
arxiv classification data data classification ensemble network random tabular data