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Overview of use of decision tree algorithms in machine learning
Abstract
A decision tree is a tree whose internal nodes can be taken as tests (on input data patterns) and whose leaf nodes can be taken as categories (of these patterns). These tests are filtered down through the tree to get the right output to the input pattern. Decision Tree algorithms can be applied and used in various different fields. It can be used as a replacement for statistical procedures to find data, to extract text, to find missing data in a class, to improve search engines and it also finds various applications in medical fields. Many Decision tree algorithms have been formulated. They have different accuracy and cost effectiveness. It is also very important for us to know which algorithm is best to use. The ID3 is one of the oldest Decision tree algorithms. It is very useful while making simple decision trees but as the complications increases its accuracy to make good Decision trees decreases. Hence IDA (intelligent decision tree algorithm) and C4.5 algorithms have been formulated.
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Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
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Alabdulhafith, M., Saleh, H., Elmannai, H., Ali, Z. H., El-Sappagh, S., Hu, J.-W., & El-Rashidy, N. (2023) · IEEE Access · doi:10.1109/access.2023.3312343 · Cited by 27
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Metadata
- Authors
- Navada, A., Ansari, A. N., Patil, S., & Sonkamble, B. A.
- Venue
- 2011 IEEE Control and System Graduate Research Colloquium
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 37-42
- OpenAlex
- W2143508752
- Cited by
- 383
- References
- 1
- FWCI
- 1.44
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Navada, A., Ansari, A. N., Patil, S., & Sonkamble, B. A. (2011). Overview of use of decision tree algorithms in machine learning. 2011 IEEE Control and System Graduate Research Colloquium. https://doi.org/10.1109/icsgrc.2011.5991826
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