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Decision tree classifier: a detailed survey

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  • Priyanka
  • Dharmender Kumar

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.

Suggested Citation

  • Priyanka & Dharmender Kumar, 2020. "Decision tree classifier: a detailed survey," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 12(3), pages 246-269.
    • Handle: RePEc:ids:ijidsc:v:12:y:2020:i:3:p:246-269
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    Cited by:

    1. Gourlay, Sydney & Kilic, Talip & Martuscelli, Antonio & Wollburg, Philip & Zezza, Alberto, 2021. "Viewpoint: High-frequency phone surveys on COVID-19: Good practices, open questions," Food Policy, Elsevier, vol. 105(C).
    2. Muhammed-Fatih Kaya, 2022. "Pattern Labelling of Business Communication Data," Group Decision and Negotiation, Springer, vol. 31(6), pages 1203-1234, December.
    3. Yichao Xie & Bowen Zhou & Zhenyu Wang & Bo Yang & Liaoyi Ning & Yanhui Zhang, 2023. "Industrial Carbon Footprint (ICF) Calculation Approach Based on Bayesian Cross-Validation Improved Cyclic Stacking," Sustainability, MDPI, vol. 15(19), pages 1-35, September.
    4. Keddell, Emily & Colhoun, Sarah & Norris, Pauline & Willing, Esther, 2024. "The heuristic divergence between community reporters and child protection agencies: Negotiating risk amidst shifting sands," Children and Youth Services Review, Elsevier, vol. 159(C).
    5. Ivana Krtolica & Dragan Savić & Bojana Bajić & Snežana Radulović, 2022. "Machine Learning for Water Quality Assessment Based on Macrophyte Presence," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
    6. Malki, Zohair & Atlam, El-Sayed & Hassanien, Aboul Ella & Dagnew, Guesh & Elhosseini, Mostafa A. & Gad, Ibrahim, 2020. "Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).

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