Guided Random Forest and its application to data approximation

• URL: http://arxiv.org/abs/1909.00659v2
• Date: Thu, 07 Aug 2025 14:46:05 GMT
• Title: Guided Random Forest and its application to data approximation
• Authors: Prashant Gupta, Aashi Jindal, Jayadeva, Debarka Sengupta,
• Abstract summary: We extend the idea of building oblique decision trees with localized partitioning to obtain a global partitioning.<n>We empirically demonstrate that global partitioning reduces the generalization error bound.<n>Results on 115 benchmark datasets show that GRAF yields comparable or better results on a majority of datasets.
• Score: 2.6149031653868993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new way of constructing an ensemble classifier, named the Guided Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique decision trees with localized partitioning to obtain a global partitioning. We show that global partitioning bridges the gap between decision trees and boosting algorithms. We empirically demonstrate that global partitioning reduces the generalization error bound. Results on 115 benchmark datasets show that GRAF yields comparable or better results on a majority of datasets. We also present a new way of approximating the datasets in the framework of random forests.

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