An Approach based Iris Flower Species Recognition Using Machine Learning Classifiers

Abstract

For data instances, classification is used to forecast group membership. Techniques for machine learning are being introduced to make the classification problem simpler. The development of a model to categorize Iris blossoms using an Artificial Neural Network (ANN) and Support Vector Machine (SVM) is the main goal of this paper. The Iris flower data set is an example of a multivariate data set. It was first presented by Ronald Fisher, a British statistician and biologist. Multi-parameter analysis of taxonomic is considered a main problem of iris flower recognition. The difficulty is in differentiating between the iris flower species (Setosa, Versicolor, and Verginica) based on the dimensions of the sepal and petal. The Iris data set would be classified by looking for patterns in the sepal and petal sizes of the Iris flower, and then determining how the pattern was predicted to form the class of the Iris flower. Experimental results illustrated that both ANN and SVM can classify iris flowers successfully by obtaining 98.66% and 97.79% of accuracy, respectively.