Modern biology has experienced an increasing use of machine learning techniques for large scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest (RF) [6] technique, which includes an ensemble of decision trees and incorporates feature selection and interactions naturally in the learning process, is a popular choice. It is nonparametric, interpretable, effic...

Random Forest is a popular machine learning tool for classification of large datasets. The Dataset classified with Random Forest Algorithm (RF) are correlated and the interaction between the features leads to the study of genome interaction. The review is about RF with respect to its variable selection property which reduces the large datasets into relevant samples and predicting the accuracy f...

Network traffic classification continues to be an interesting subject among numerous networking communities. This method introduces multi-beneficial solutions in different avenues, such as network security, network management, anomaly detection, and quality-of-service. In this paper, we propose a supervised machine learning method that efficiently classifies different types of applications usin...

Random Forests (RF) are a successful ensemble prediction technique that uses majority voting or averaging as a combination function. However, it is clear that each tree in a random forest may have a different contribution in processing a certain instance. In this paper, we demonstrate that the prediction performance of RF may still be improved in some domains by replacing the combination functi...

Recent random-forest (RF)-based image super-resolution approaches inherit some properties from dictionary-learning-based algorithms, but the effectiveness of the properties in RF is overlooked in the literature. In this paper, we present a novel feature-augmented random forest (FARF) for image super-resolution, where the conventional gradient-based features are augmented with gradient magnitude...

In machine learning system different types of approaches, machine learning strategies have applications are related sentiment analysis, classification approaches, data mining etc. Irregular Forest has huge capability of turning into a prevalent method for future classifiers in light of the fact that its execution has been observed to be practically identical with troupe strategies sacking and b...

Sustainable urban planning and management require reliable land change models, which can be used to improve decision making. The objective of this study was to test a random forest-cellular automata (RF-CA) model, which combines random forest (RF) and cellular automata (CA) models. The Kappa simulation (KSimulation), figure of merit, and components of agreement and disagreement statistics were ...

Random Forest (RF) is an ensemble supervised machine learning technique. Based on bagging and random feature selection, number of decision trees (base classifiers) is generated and majority voting is taken among them. The size of RF is subjective and varies from one dataset to another. Furthermore due to the randomization induced during creation, and its huge size, RF has at best been described...