Feature Selection Using Differential Evolution for Unsupervised Image Clustering
Due to the accelerated growth of unlabeled data, unsupervised classification methods have become of great importance, and clustering is one of the main approaches among these methods. However, the performance of any clustering algorithm is highly dependent on the quality of the features used for the task. This work presents a Differential Evolution algorithm for maximizing an unsupervised clustering measure. Results are evaluated using unsupervised clustering metrics, suggesting that the Differential Evolution algorithm can achieve higher scores when compared to other feature selection methods.
KeywordsDifferential evolution Feature selection Image clustering
Author M. Gutoski and L.T. Hattori would like to thank CAPES for the scholarship; Author M. Ribeiro would like to thank the Catarinense Federal Institute of Education, Science and Technology and IFC/CAPES/Prodoutoral for the scholarship; Author N. Aquino would like to thank the Organization of the American States, the Coimbra Group of Brazilian Universities and the Pan American Health Organization; author H. S. Lopes would like to thank to CNPq for the research grant number 440977/2015-0.
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