Results 21 to 30 of about 982,398 (277)

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

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

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

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

A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA

Barekeng
Random forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem.
Arie Purwanto, Bagus Sartono, Khairil Anwar Notodiputro   +2 more
doaj   +1 more source

Investigation of the possibility of landslide hazard mapping using the Random Forest algorithm (Case study: Sardarabad Watershed, Lorestan Province) [PDF]

مخاطرات محیط طبیعی, 2018
With respect to the ability of data analysis techniques, their applications in various engineering and geosciences disciplines have been expanded. In this study, the random forest algorithm has been used for landslide susceptibility mapping in the ...
Ali Talebi, Sahar Goudarzi, Hamid Reza Pourghsemi Pourghsemi   +2 more
doaj   +1 more source

SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java

Journal of Applied Informatics and Computing
Random Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance.
Erwan Setiawan, Bagus Sartono, Khairil Anwar Notodiputro   +2 more
doaj   +1 more source