Machine Learning Classifiers Based Classification For IRIS Recognition


  • Bahzad Taha Chicho Duhok Polytechnic University Duhok, Iraq
  • Adnan Mohsin Abdulazeez President of Duhok Polytechnic University Duhok, Iraq
  • Diyar Qader Zeebaree Research Center Duhok Polytechnic University, Duhok, Iraq
  • Dilovan Assad Zebari Research Center Duhok Polytechnic University, Duhok, Iraq



Data Mining, Classification, Decision Tree, Random Forest, K-nearest neighbors


Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained.


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How to Cite

Taha Chicho, B., Mohsin Abdulazeez, A., Qader Zeebaree, D., & Assad Zebari, D. (2021). Machine Learning Classifiers Based Classification For IRIS Recognition. Qubahan Academic Journal, 1(2), 106–118.




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