Pattern Recognition of Iris flower using Neural Network based Particle Swarm Optimization - Mane, D. T; Kulkarni, U. V (International Journal of Computer Sciences and Engineering, 2025)
In machine learning, classification is a technique to identify the category to which an observation belongs, based on a labeled training data. It is a task of approximating a mapping function from input variables to discrete output variables. Pattern classification delivers this approximation by automatically discovering the regularities in the data using learning algorithms. It is an important sub-topic of machine learning with interesting applications like speech recognition and classification of rocks. In this paper, propose a hybrid approach Artificial Neural Network with Particle Swarm Optimization (ANNPSO) algorithm for pattern recognition. The ANNPSO works under the two main principles, first the error is calculated by using artificial neural network and second, error is optimized using Particle swarm optimization algorithms. Model tested on well known standard pattern IRIS flower dataset. Performance of presented model is evaluated with five-fold cross validation which produces 99.33% testing accuracy. Experimental results are superior than the existing ones. Therefore, ANNPSO provides better testing results in Iris pattern classification problems.