Random forests: from early developments to recent advancements

By Khaled Fawagreh, Mohamed Medhat Gaber, Eyad Elyan. 2014

701 citations recorded by the source corpus.

Ensemble classification is a data mining approach that utilizes a number of classifiers that work together in order to identify the class label for unlabeled instances. Random forest (RF) is an ensemble classification approach that has proved its high accuracy and superiority. With one common goal in mind, RF has recently received considerable attention from the research community to further boost its performance. In this paper, we look at developments of RF from birth to present. The main aim is to describe the research done to date and also identify potential and future developments to RF. Our approach in this review paper is to take a historical view on the development of this notably successful classification technique. We start with developments that were found before Breiman's introduction of the technique in 2001, by which RF has borrowed some of its components. We then delve into dealing with the main technique proposed by Breiman. A number of developments to enhance the original technique are then presented and summarized. Successful applications that utilized RF are discussed, before a discussion of possible directions of research is finally given.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Mathematical optimization in classification and regression trees (2021, background): “Purity, although popular, only measures classification accuracy in an indirect way, and therefore may not yield good generalization results (Fayyad and Irani 1992), and other criteria, such as Maximum Likelihood (Su et al. 2004), have been proposed.”
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, neutral): “Gradient ], [24].”
Machine Learning in Aerodynamic Shape Optimization (2022, background): “A random forest [138, 139] assembles a series of decision trees that contribute to the classification/regression problems from broader aspects (Fig.”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “Next, a voting scenario is designed to assign a label to unlabeled samples [11], [103]– [105].”
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques (2021, methodology): “Ensemble learning methods employ multiple learning algorithms to generate an optimal predictive model, which can provide better results than any of the individual model’s prediction [101].”
Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model (2021, background): “…is selected in this study because of many advantages, such as the prediction accuracy, fast simulation speed, robustness to noise, and overﬁtting [37], and easily parallelized, especially useful for Monte Carlo simulations and useful for error estimation and determination of variable importance.”