Feb 24, 2020 · Further, a novel classifier ensemble pruning framework (HDRFPF) based on HDRF is designed to remove redundant and invalid classifiers.
The classification of high-dimensional data is a challenge in machine learning. Traditional classifier ensemble methods improve the diversity of classifiers ...
3) Considering the influence of redundant classifiers for the integrated system, an ensemble forest pruning process is proposed to remove redundant classifiers;
Mar 6, 2020 · Experimental results on 23 high-dimensional data sets verify that our method outperforms mainstream classifier ensemble methods, and the better ...
The classification of high-dimensional data is a challenge in machine learning. Traditional classifier ensemble methods improve the diversity of classifiers ...
Duration: 1:53
Posted: Jun 20, 2020
Posted: Jun 20, 2020
Sep 24, 2022 · Summary: Is random forest naturally more robust to bad features in high vs low dimensional datasets? I've made a number of text classifiers ...
Missing: Hybrid Pruning
Oct 15, 2022 · Random forests, also known as random selection forests, are an ensemble learning approach for classification, regression, and other problems.
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Two of the more common approaches to addressing these challenges of high dimensional spaces are reducing the dimensionality of the dataset or applying methods ...
Dec 30, 2021 · The proposed method, Hybrid Dimensionality Reduction Forest (HDRF) with Random Forest (RF) ensemble classifier and kappa measure, were used to overcome those ...