Classifying Flowers Images by using Different Classifiers in Orange
Vijaylakshmi Sajwan1, Rakesh Ranjan2
1Ms. Vijaylakshmi Sajwan, Assistant Professor, Uttaranchal Institute of Management, Uttaranchal University.
2Prof. Dr. Rakesh Ranjan, Pro-Vice Chancellor, Himgiri Zee University.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1057-1061 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13340986S319/19©BEIESP | DOI: 10.35940/ijeat.F1334.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Logistic Regression, Neural Network, AdaBoost, SVM, Random Forest, K-NN, Supervised Machine Learning
Scope of the Article: Image Processing and Pattern Recognition