Open AccessClassifying Flowers Images by using Different Classifiers in Orange
Author(s) -
Mohit Sajwan,
Prabhat Ranjan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f1334.0986s319
Subject(s) - artificial intelligence , random forest , support vector machine , pattern recognition (psychology) , adaboost , random subspace method , computer science , artificial neural network , machine learning , contextual image classification , feature extraction , logistic regression , image (mathematics)
This paper presents the first step towards looking for an advanced solution of image classification using distinct Classifiers in the Orange Data Mining Tool. The objective of the paper is to decide the ability of distinct classifiers for flowers image classification involving a small sample; Deep learning models are used to calculate a feature vector for every image of the Iris flower database. The used classifiers involved logistic regression, Neural Network, AdaBoost, Support Vector Machine, Random Forest and K-NN. The result indicates that the Logistic Regression, Neural Network, AdaBoost classifiers perform best in classifying a small sample of Iris flower images, and SVM and Random Forest classifiers perform less classification accuracy then above classifiers while K-NN performs worst with the lowest classification accuracy.