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Machine Learning Classification Based on Radom Forest Algorithm: A Review

Author

Listed:
  • Nasiba Mahdi Abdulkareem

    (Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq)

  • Adnan Mohsin Abdulazeez

    (Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq)

Abstract

Machine Learning is a significant technique to realize Artificial Intelligence. The Random Forest Algorithm can be considered as one of the Machine Learning’s representative algorithm, which is known for its simplicity and effectiveness. It is also can be defined as a Decision Tree-Based Classifier that chooses the best classification tree as the final classifier's classification of the algorithm via voting. Random Forest is the most accepted group classification technique because of having excellent features such as Variable Importance Measure, Out-of-bag error, Proximities, etc. Currently, it is in the new classification, intrusion detection, content information filtering, and sentiment analysis that is why there is an extensive range of applications in image processing. In this paper, the construction process of Random Forests and the study status of Random Forests would primarily be introduced in terms of capacity enhancement and performance indicators. The use of Random Forest in different fields such as Medicine, Agriculture, Astronomy, etc. is often mentioned.

Suggested Citation

  • Nasiba Mahdi Abdulkareem & Adnan Mohsin Abdulazeez, 2021. "Machine Learning Classification Based on Radom Forest Algorithm: A Review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 128-142.
    • Handle: RePEc:aif:journl:v:5:y:2021:i:2:p:128-142
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    Cited by:

    1. Noor Salah Hassan & Nawzat Sadiq Ahmed, 2021. "A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 21-32.
    2. Shler Farhad Khorshid & Nawzat Sadiq Ahmed, 2021. "A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 12-20.

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