2008
DOI: 10.1016/j.knosys.2008.03.051
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Feature selection based-on genetic algorithm for image annotation

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Cited by 90 publications
(34 citation statements)
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“…This approach has proven especially useful with large data sets, where standard feature selection techniques are computationally expensive. GA with fitness function based on the classification accuracy of k-nearest neighbor and features subset complexity was used to improve the performance of image annotation system (Lu et al, 2008). Li et al (2001) combined GA and k-nearestneighbor to select feature (genes) that can jointly discriminate between different classes of samples (e.g.…”
Section: Fitness Functionmentioning
confidence: 99%
“…This approach has proven especially useful with large data sets, where standard feature selection techniques are computationally expensive. GA with fitness function based on the classification accuracy of k-nearest neighbor and features subset complexity was used to improve the performance of image annotation system (Lu et al, 2008). Li et al (2001) combined GA and k-nearestneighbor to select feature (genes) that can jointly discriminate between different classes of samples (e.g.…”
Section: Fitness Functionmentioning
confidence: 99%
“…Reducing attributes can save cost of computational time and memory. It is also useful to improve classification accuracy as a result of removing redundant and irrelevant features [1,2].…”
Section: Introductionmentioning
confidence: 99%
“…Multimedia Content Description Interface (MPEG-7) is one of the most famous multimedia metadata standards, which includes a number of image feature descriptors to represent low-level features of images effectively [4,[9][10][11]. Although these features describe the characteristics of images from different views, there are redundancies among them.…”
Section: Introductionmentioning
confidence: 99%