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Comparison of a Classifier Performance Testing Methods: Support Vector Machine Classifier on Mammogram Images Classification

Author: Sura Jasim Mohammed
Journal: Journal of Kufa for Mathematics and Computer مجلة الكوفة للرياضيات والحاسوب ISSN: 11712076 Year: 2019 Volume: 6 Issue: 1 Pages: 8-12
Publisher: University of Kufa جامعة الكوفة

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Abstract

This paper compares between testing performance methods of classifier algorithm on a standard database of mammogram images. Mammographic interchange society dataset (MIAS) is used in this work. For classifying these images tumors a multiclass support vector machine (SVM) classifier is used. Evaluating this classifier accuracy for classifying the mammogram tumors into the malignant, benign or normal case is done using two evaluating classifier methods that are a hold-out method and one of the cross-validation methods. Then selecting the better test method depending on the obtained classifier accuracy and the running time consumed with each method. The classifier accuracy, training time and the classification time are considered for comparison purpose

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