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2018 | OriginalPaper | Chapter

Feature Selection Using Differential Evolution for Unsupervised Image Clustering

Authors : Matheus Gutoski, Manassés Ribeiro, Nelson Marcelo Romero Aquino, Leandro Takeshi Hattori, André Eugênio Lazzaretti, Heitor Silvério Lopes

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Genetic Programming for the Classification of Levels of Mammographic Density
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Footnotes
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Metadata
Title
Feature Selection Using Differential Evolution for Unsupervised Image Clustering
Authors
Matheus Gutoski
Manassés Ribeiro
Nelson Marcelo Romero Aquino
Leandro Takeshi Hattori
André Eugênio Lazzaretti
Heitor Silvério Lopes
Copyright Year
2018
Book
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-91253-0_35

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