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Abstract
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Article Information:
Effective Rule Based Classifier using Multivariate Filter and Genetic Miner for Mammographic Image Classification
Nirase Fathima Abubacker, Azreen Azman, Masrah Azrifah Azmi Murad and Shyamala Doraisamy
Corresponding Author: Nirase Fathima Abubacker
Submitted: February 8, 2015
Accepted: March 7, 2015
Published: June 15, 2015 |
Abstract:
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Mammography is an important examination in the early detection of breast abnormalities. Automatic classifications of mammogram images into normal, benign or malignant would help the radiologists in diagnosis of breast cancer cases. This study investigates the effectiveness of using rule-based classifiers with multivariate filter and genetic miner to classify mammogram images. The method discovers association rules with the classes as the consequence and classifies the images based on the Highest Average Confidence of the association rules (HAvC) matched for the classes. In the association rules mining stage, Correlation based Feature Selection (CFS) plays an enormous significance to reduce the complexity of image mining process is used in this study as a feature selection method and a modified genetic association rule mining technique, the GARM, is used to discover the rules. The method is evaluated on mammogram image dataset with 240 images taken from DDSM. The performance of the method is compared against other classifiers such as SMO; Naïve Bayes and J48. The performance of the proposed method is promising with 88% accuracy and outperforms other classifiers in the context of mammogram image classification.
Key words: Association rule mining, correlation-based feature selection, mammographic image classification, multivariate filters , , ,
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Cite this Reference:
Nirase Fathima Abubacker, Azreen Azman, Masrah Azrifah Azmi Murad and Shyamala Doraisamy, . Effective Rule Based Classifier using Multivariate Filter and Genetic Miner for Mammographic Image Classification. Research Journal of Applied Sciences, Engineering and Technology, (5): 591-598.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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