Frontiers

Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery

Introduction: Accurate identification of forest tree species is essential for sustainable forest management, biodiversity assessment, and environmental monitoring. Urban forests, in particular, ...
Frontiers

A random forest dynamic threshold imputation method for handling missing data in cognitive diagnosis assessments

The handling of missing data in cognitive diagnostic assessment is an important issue. The Random Forest Threshold Imputation (RFTI) method proposed by You et al. in 2023 is specifically designed for ...
Scientific Research Publishing

Adugna, T., Xu, W., & Fan, J. (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images ...

ABSTRACT: Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion, especially in Tiaty, Baringo, Kenya. These issues create mixed opportunities for pastoral ...
Scientific Research Publishing

Pazhanikumar, K. and KuzhalVoiMozhi, S.N. (2023) Remote Sensing Image Classification Using Modified Random Forest with Empirical Loss Function through Crowd-Sourced Data ...

ABSTRACT: In this study, multi-source remote sensing data and machine learning algorithms were used to delineate the prospect area of remote sensing geological prospecting in eastern Botswana. Landsat ...
IEEE

A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition

Abstract: Facial emotion recognition (FER) has been applied to various sectors, including e-learning, marketing, humanoid robot design, HMI/HCI applications, and medicine. The rapid development of ...
C&EN

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.
IEEE

An Optimized Random Forest Classification Method for Processing Imbalanced Data Sets of Alzheimer's Disease

Abstract: In this work we propose an optimized random forest classification method to solve the problem of imbalanced samples in the Alzheimer's disease (AD). The improved algorithm is based on ...