 Decision tree classifier: a detailed surveyPriyanka and Dharmender Kumar International Journal of Information and Decision Sciences, 2020, vol. 12, issue 3, 246-269 Abstract: Decision tree classifier (DTC) is one of the well-known methods for data classification. The most significant feature of DTC is its ability to change the complicated decision making problems into simple processes, thus finding a solution which is understandable and easier to interpret. This paper provides a brief review on various algorithms developed in literature for constructing and representing decision trees, splitting criteria for selecting best attribute and pruning methods. The readers will be able to understand why decision trees are more popular among all other methods of classification, what are their uses, limitations and applications in different diverse areas. They will also come to know about a decision tree induction algorithms, splitting criteria, pruning methods, concepts of ensemble methods, fuzzy decision trees, hybridisation of DTCs, etc. These enhancements are found very helpful in solving complex datasets with less computation in very short time period while achieving high accuracy. Keywords: decision tree hybridisation; classification; iterative dichotomiser 3; ID3; CART; ensembles; splitting criteria; pruning methods. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijidsc:v:12:y:2020:i:3:p:246-269 Access Statistics for this article More articles in International Journal of Information and Decision Sciences from Inderscience Enterprises Ltd |
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