Analysis of Naive Bayesian and Back Propagation algorithms in iris classification

Abstract

An efficient taxonomy of irises can provide botanists with valuable tools. Machine learning algorithms can effectively improve the performance of iris classification models because they can automatically analyze and summarize data. To this end, this paper introduces Naive Bayesian (NB) and Back Propagation (BP) to build classification models. When creating the NB model, the petal and sepal data from the iris dataset are used sequentially as classification criteria to classify the data. When constructing the BP model, the author sets different iterations and outputs the loss function and accuracy of the BP model under different iterations. The study finds that the NB model has higher classification accuracy when using petal length and petal width as classification criteria, which is 17% higher than the classification accuracy using sepal length and sepal width. Therefore, the NB model is more suitable for classifying independent data. By studying the use of the BP algorithm to classify iris flowers, the automatic classification of iris flowers can be realized and the accuracy of classification can be improved. Compared with the traditional NB algorithm, the BP algorithm can better mine the hidden patterns and information in the iris data and make effective classifications. This study provides new insights and discoveries for the taxonomic study of Iris plants.