Manuscript Number : CSEIT183821
Multiclass Classification Using Random Forest Classifier
Authors(4) :-Sermista Talla, Pavani Venigalla, Ashmitha Shaik, Meghana Vuyyuru A multiclass classification using Random Forest Classifier is proposed in this paper. The Random forest classifier is commonly used for solving the multiclass classification tasks in machine learning. The Random forest classifier predicts a valid classification results with minimum training time when compared to other classifier algorithms. For pattern classification tasks on an image dataset are performed in order to evaluate the performance of the proposed classifier. First, we consider some sample datasets of flower species and birds. Second, we train the object class models by using datasets to develop the random forest classifier for image classification. Third, we test the object class models and find the accuracy .In this we use Haralick,Hu moments to extract global features to quantify an image. In the experiments we test our method using FLOWERS-17,CUB_200_2011 dataset and compare the results with other methods like logistic regression, k-nearest neighbour, support vector machine.
Sermista Talla Random Forest Classifier, Image Classification, Haralick, Hu Moments, Histogram. Publication Details Published in : Volume 5 | Issue 2 | March-April 2019 Article Preview
Computer Science and Engineering Department, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
Pavani Venigalla
Computer Science and Engineering Department, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
Ashmitha Shaik
Computer Science and Engineering Department, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
Meghana Vuyyuru
Computer Science and Engineering Department, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
Date of Publication : 2019-04-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 493-496
Manuscript Number : CSEIT183821
Publisher : Technoscience Academy
Journal URL : https://res.ijsrcseit.com/CSEIT183821
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