DecisionTree for Classification and Regression: A State-of-the Art Review

Monalisa Jena, Satchidananda Dehuri


Classification and regression are defined under the umbrella of the prediction task of data mining. Discrete values are predicted using classification techniques whereas regression techniques are most suitable for predicting continuous data. Analysts from different research areas like data mining, statistics, machine learning, pattern recognition, and big data analytics preferred decision trees over other classifiers as it is simple, effective, efficient, and its performance is competitive with others. In this paper, we review extensively many popularly used state-of-the-art
decision tree-based techniques for classification and regression. We present a survey of more than forty years of research that has been emphasized on the application of decision trees in both classification and regression. This survey could be the potential source for all the researchers who are keenly interested to apply the decision tree classifier/regressor for their research work.

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