Comparative study of Jrip j48 and naive bayes algorithm in Flower specie prediction

Abstract

Artificial intelligence and Deep learning techniques propose and provide effective mechanism for classification among several commodities like gender classification on basis of ridge count, spam mail classification and detection etc. Here we have proposed a hybrid module on basis of available AI and ML techniques by which we can achieve more than 90% accuracy for any provided test dataset. We have included over 50423 samples, out of which we use 66 percentage of data for training purpose of our model and 34% remaining sample we use as test dataset, with 8 fold we have achieve approximately 96% accuracy. For collection of dataset and processing of dataset we gone through several phases which include extraction of feature (EoF) using feature extraction technique, for cleaning of dataset we have use dimension reduction technique factor analysis(FA). In next phase for classification of flowers we have used classification techniques in which we found the accuracy score of j48 classifier is higher than Naïve Bayes and Jrip algorithm. Hence the choice of j48 classifier for classification is right.