Machine Learning Classifiers Based Classification For IRIS Recognition

Bahzad Taha Chicho, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dilovan Assad Zebari

2021Qubahan Academic Journal34 citationsDOI Open Access PDF

Abstract

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.

Topics & Concepts

C4.5 algorithmComputer scienceDecision treeRandom forestArtificial intelligencePattern recognition (psychology)Classifier (UML)Machine learningCluster analysisRandom subspace methodk-nearest neighbors algorithmData miningNaive Bayes classifierSupport vector machineComputer Science and EngineeringData Mining and Machine Learning ApplicationsInformation Retrieval and Data Mining