Results 41 to 50 of about 6,285,017 (364)

Prediction of prognosis and survival of patients with gastric cancer by a weighted improved random forest model: an application of machine learning in medicine

Archives of Medical Science, 2021
Introduction It is essential to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random forest is an excellent machine learning algorithm even without any modification.
Cheng Xu, Jing Wang, Tianlong Zheng, Yue Cao, Fan Ye   +4 more
doaj   +1 more source

Crop Yield Prediction Using Improved Random Forest [PDF]

ITM Web of Conferences, 2023
Agriculture has an important role in India’s economic development. Crop productivity is affected by the rising population and the country’s ever-changing climate. Crop yield estimation is a challenge in the farming sector.
T. Padma, Sinha Dipali
doaj   +1 more source

Enhancing random forests performance in microarray data classification [PDF]

, 2013
Random forests are receiving increasing attention for classification of microarray datasets. We evaluate the effects of a feature selection process on the performance of a random forest classifier as well as on the choice of two critical parameters, i.e.
DESSI, NICOLETTA, MILIA, GABRIELE, PES, BARBARA   +2 more
core   +1 more source

Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data

Computational and Structural Biotechnology Journal, 2022
Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses.
Angelica M. Walker, Ashley Cliff, Jonathon Romero, Manesh B. Shah, Piet Jones, Joao Gabriel Felipe Machado Gazolla, Daniel A Jacobson, David Kainer   +7 more
doaj   +1 more source

Improving random forest predictions in small datasets from two-phase sampling designs

BMC Medical Informatics and Decision Making, 2021
Background While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases—a common situation in ...
Sunwoo Han, B. Williamson, Y. Fong
semanticscholar   +1 more source

DATA MINING USING RANDOM FOREST, NAÏVE BAYES, AND ADABOOST MODELS FOR PREDICTION AND CLASSIFICATION OF BENIGN AND MALIGNANT BREAST CANCER

Pilar Nusa Mandiri, 2022
This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost.
Bahtiar Imran, Hambali Hambali, Ahmad Subki, Zaeniah Zaeniah, Ahmad Yani, Muhammad Rijal Alfian   +5 more
doaj   +1 more source

Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping

Frontiers in Earth Science, 2021
Landslides are widely distributed worldwide and often result in tremendous casualties and economic losses, especially in the Loess Plateau of China.
Shibao Wang, J. Zhuang, Jia Zheng, Hongyu Fan, Jia-xu Kong, Jiewei Zhan   +5 more
semanticscholar   +1 more source

Ransomware Detection using Random Forest Technique

ICT Express, 2020
Nowadays, the ransomware became a serious threat challenge the computing world that requires an immediate consideration to avoid financial and moral blackmail. So, there is a real need for a new method that can detect and stop this type of attack.
Ban Mohammed Khammas
doaj   +1 more source

The Impact of Simulated Spectral Noise on Random Forest and Oblique Random Forest Classification Performance

Journal of Spectroscopy, 2018
Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable ...
Na’eem Hoosen Agjee, Onisimo Mutanga, Kabir Peerbhay, Riyad Ismail   +3 more
doaj   +1 more source

Random Forest Prediction of IPO Underpricing

Applied Sciences, 2017
The prediction of initial returns on initial public offerings (IPOs) is a complex matter. The independent variables identified in the literature mix strong and weak predictors, their explanatory power is limited, and samples include a sizable number of ...
David Quintana, Yago Sáez, Pedro Isasi
doaj   +1 more source