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Multi-objective Techniques for Feature Selection and Classification in Digital Mammography

What is it about?

The proposed article investigates multi-objective techniques for feature selection and classification in digital mammography. The methodology uses multi-objective particle swarm optimization and Nondominated sorting genetic algorithm-III for feature selection. The optimal features are selected from Pareto-optimal fronts generated by MOPSO and NSGA-III for two conflicting objective functions; the number of selected features and the mean squared error. The features selected are used to predict breast tissue as benign or malignant.

Why is it important?

The proposed system can assist in improving breast cancer diagnosis and reduce the mortality rate.

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The following have contributed to this page:
Shankar Thawkar
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