A self explanatory review of decision tree classifiers

Abstract

Decision tree classifiers are considered to serve as a standout amongst the most well-known approaches for representing classifiers in data classification. The issue of expanding a decision tree from available data has been considered by various researchers from diverse realms and disciplines for example machine studying, pattern recognition and statistics. The utilization of Decision tree classifiers have been suggested multifariously in numerous areas like remote sensing, speech recognition, medicinal analysis and numerous more. This paper gives brief of various known algorithms for representing and constructing decision tree classifiers. In addition to it, various pruning methodologies, splitting criteria and ensemble methods are also discussed. In short, the paper presents a short self-explanatory review of decision tree classification which would be beneficial for beginners.