Frontiers

Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...
IEEE

Leaf–Wood Separation in Tree Point Clouds via Unlearning-Driven Supervoxel Random Forest

Abstract: Accurate separation of wood and leaf components from tree point clouds is essential for precise tree parameter estimation and high-fidelity 3-D model reconstruction. However, deep learning ...
Frontiers

Prediction of bacteremia using routine hematological and metabolic parameters based on logistic regression and random forest models

Background: This study aimed to evaluate the predictive utility of routine hematological, inflammatory, and metabolic markers for bacteremia and to compare the classification performance of logistic ...
IEEE

Maximum Depth Parameter Explorations of Random Forest Method For Breast Cancer Classification On RNA Mutations Data

Abstract: One kind of cancer that affects women globally is breast cancer. Machine learning techniques are a possible alternative approach to increase the precision of breast cancer detection. One ...
Scientific Research Publishing

Kirasich, K., Smith, T. and Sadler, B. (2018) Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets. SMU Data Science Review, 1, Article 9.

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
Scientific Research Publishing

Remote Sensing Prospecting Method Based on Random Forest in Eastern Botswana Mining Area ()

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 8 remote ...