Please use this identifier to cite or link to this item:
|Type:||Artigo de periódico|
|Title:||Data feature selection based on Artificial Bee Colony algorithm|
|Abstract:||Classification of data in large repositories requires efficient techniques for analysis since a large amount of features is created for better representation of such images. Optimization methods can be used in the process of feature selection to determine the most relevant subset of features from the data set while maintaining adequate accuracy rate represented by the original set of features. Several bioinspired algorithms, that is, based on the behavior of living beings of nature, have been proposed in the literature with the objective of solving optimization problems. This paper aims at investigating, implementing, and analyzing a feature selection method using the Artificial Bee Colony approach to classification of different data sets. Various UCI data sets have been used to demonstrate the effectiveness of the proposed method against other relevant approaches available in the literature.|
|Editor:||Springer International Publishing Ag|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.