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Related Experiment Video

Updated: Sep 5, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Oblique and rotation double random forest.

M A Ganaie¹, M Tanveer¹, P N Suganthan²

¹Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, India.

Neural Networks : the Official Journal of the International Neural Network Society

|July 11, 2022

Summary

This summary is machine-generated.

This study introduces novel oblique and rotation double random forests, enhancing machine learning model diversity and performance. These advanced random forest algorithms improve generalization by capturing complex data structures and addressing small sample size issues.

Keywords:

Bootstrap Decision tree Double random forest Ensemble learning Oblique random forest classification

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Machine Learning
Ensemble Methods
Data Mining

Background:

Random Forest (RF) is a powerful ensemble method utilizing bagging and random subspace concepts.
The strength of RF lies in the diversity and instability of its base learners (decision trees).
Standard RF uses univariate decision trees, which may not capture data's geometric structure effectively.

Purpose of the Study:

To propose two novel ensemble methods: rotation double random forest and oblique double random forest.
To enhance the diversity of base learners and improve generalization performance.
To address limitations of standard RF, such as axis-parallel splits and suboptimal tree depth.

Main Methods:

Rotation Double Random Forest: Applies feature space transformations (PCA, LDA) at each node for increased base learner diversity.
Oblique Double Random Forest: Employs multivariate decision trees with optimal plane generation using multisurface proximal SVM and regularization techniques for small sample sizes.
Bagging is applied at each non-leaf node to promote larger tree sizes and improved performance.

Main Results:

Both proposed methods demonstrated improved performance compared to baseline models.
Evaluation on 121 UCI benchmark datasets and real-world fisheries datasets confirmed the efficacy of the new models.
Statistical analysis and experimental results validated the superiority of oblique and rotation double random forests.

Conclusions:

The proposed rotation and oblique double random forest models offer significant improvements over standard ensemble methods.
These advanced techniques effectively capture data's geometric properties and mitigate small sample size challenges.
The enhanced diversity and structure of the proposed models lead to superior generalization performance.