![]() More formally, assume the training set is and where X represents the set of predictive attributes in the training set, m is the number of predictive attributes in the training set, n is the number of instances in the training set, and y represents the set of z possible class labels. The model is then applied to predict the class labels for the unclassified objects in the testing data as shown in Figure 2. The classification algorithm learns from the training set and builds a model, also called a classifier, as shown in Figure 1. Classification algorithms normally use a training set where all objects are already associated with known class labels. The relevant task to us in this paper is classification which organizes data into classes by using predetermined class labels. (1996), data mining involves six common classes of tasks: anomaly detection, association rule learning, clustering, classification, regression, summarization, and sequential pattern mining. It is interesting because it applies methods at the intersection of multiple disciplines including artificial intelligence, machine learning, statistics, and database systems ( CitationFayyad et al., 1996).Īs stated in CitationFayyad et al. It is important because it specializes in analyzing the data from different perspectives and summarizing it into useful information – information that can be used to increase revenues, cut costs, or both. Successful applications that utilized RF are discussed, before a discussion of possible directions of research is finally given.ĭata mining is an important and interesting field in Computer Science and has received a lot of attention from the research community particularly over the past decade. A number of developments to enhance the original technique are then presented and summarized. We then delve into dealing with the main technique proposed by Breiman. 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. Our approach in this review paper is to take a historical view on the development of this notably successful classification technique. The main aim is to describe the research done to date and also identify potential and future developments to RF. In this paper, we look at developments of RF from birth to present. With one common goal in mind, RF has recently received considerable attention from the research community to further boost its performance. Random forest (RF) is an ensemble classification approach that has proved its high accuracy and superiority. 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.
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