Improving Iris Dataset Classification Prediction Achievement By Using Optimum k Value of kNN Algorithm
Ahmet Çelik · Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi · 2022
Machine learning methods are widely used in automated technologies. Classification prediction is a machine learning based on data mining. Today, many technological devices can make new predictions by gaining experience from past data with machine learning methods. Machine learning is widely studied in two types, supervised and unsupervised. The limits of the objectives in supervised learning are predetermined. In unsupervised learning, there are no predetermined targets. In this learning, the machines are required to determine the targets automatically. Prediction process is one of the basic components of machine learning. Machines need to use some algorithms in order to perform the prediction process on the basis of data mining. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. These algorithms can be applied with the help of some tools on data sets. In this study, kNN algorithm was used to estimate Iris data set classification using Orange tool. The success of the KNN algorithm depends on using the correct attribute and changing the optimum k value. As a result of the tests, it was determined that when the k neighbor value was selected as 15, it was the most suitable k neighbor value, providing 98.67% classification prediction success in the Iris dataset.