Usupervised Ensemble Selection for Multilayer Bootstrap Networks


This page is still under construction....(last revised on July 5, 2021)

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This project proposes three algorithms for determining the optimal network structure of multilayer bootstrap networks automatically:

1. MBN-E: an Ensemble of MBN base models that have different network structure;

2. MBN-SO: it first conducts clustering on the output of MBN-E, and then uses the clustering result as a guidance to select a small number of base models whose output representation is the most discriminant. Finally, it uses the selected base models to group into a new MBN-E.

3. MBN-SD: It uses the meta-representation produced by MBN-E as a reference for selecting a small number of base models whose output representation is the most discriminant. Finally, it uses the selected base models to group into a new MBN-E.

The source code (still dirty) with demos and datasets is downloadable here: [MBN_E.rar] (49.97MB)

References:

Xiao-Lei Zhang. Unsupervised ensemble selection for multilayer bootstrap networks. arXiv preprint arXiv:2107.02071. 2021.

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