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Updated: Jul 3, 2026

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

Enriched random forests.

Dhammika Amaratunga¹, Javier Cabrera, Yung-Seop Lee

¹Department of Nonclinical Biostatistics, Johnson & Johnson PRD LLC, Raritan, NJ 08869, USA. damaratu@prdus.jnj.com

Bioinformatics (Oxford, England)

|July 25, 2008

Summary

This summary is machine-generated.

Random forest classification struggles with massive datasets containing few informative features. An "enriched random forest" improves performance by weighting informative features during node selection, enhancing accuracy in DNA microarray analysis.

Related Experiment Videos

Last Updated: Jul 3, 2026

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

Area of Science:

Bioinformatics
Machine Learning
Genomics

Background:

Random forest classification is effective for high-dimensional data.
Performance degrades with a large number of features and few informative ones, common in DNA microarrays.
Reducing the influence of non-informative features can improve random forest models.

Purpose of the Study:

To propose a novel adjustment to the random forest algorithm to enhance its performance on datasets with a high feature-to-sample ratio.
To address the decline in random forest accuracy when dealing with datasets where informative features are scarce, such as in gene expression data.

Main Methods:

Introduced a weighted random sampling method for selecting feature subsets at each node.
Tilted the sampling weights to favor informative features over non-informative ones.
Developed an 'enriched random forest' algorithm based on this weighted sampling approach.

Main Results:

The 'enriched random forest' demonstrated superior performance compared to standard random forest methods.
The proposed method showed significant improvements on several real-world DNA microarray datasets.
Weighted sampling effectively reduced the impact of non-informative features on tree construction.

Conclusions:

The 'enriched random forest' offers a simple yet effective enhancement for classification tasks with high-dimensional, sparse data.
Weighted random sampling is a viable strategy to improve the robustness and accuracy of random forests in bioinformatics.
This approach provides a practical solution for analyzing complex genomic datasets like DNA microarrays.