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Feature selection and multiple classifier fusion using genetic algorithms in intrusion detection systems 

Erdem, Hamit; Ozgur, Atilla (2018)
With the improvements in information systems, intrusion detection systems (IDS) become more important. IDS can be thought as a classification problem. An important step of classification applications is feature selection ...
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Sparsity-driven weighted ensemble classifier 

Erdem, Hamit; Ozgur, Atilla; Nar, Fatih (2018)
In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which ...
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RmSAT-CFAR: Fast and accurate target detection in radar images 

Nar, Fatih; Okman, Osman Erman; Ozgur, Atilla; Cetin, Mujdat (2018)
As the first step of automatic image interpretation systems, automatic detection of the targets should be accurate and fast. Constant False Alarm Rate (CFAR) is the most popular target detection framework for Synthetic ...

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Ozgur, Atilla (3)
Erdem, Hamit (2)Nar, Fatih (2)Cetin, Mujdat (1)Okman, Osman Erman (1)SubjectAdaptive simulated annealing (1)CFAR (1)Classification (1)Classifier Ensembles (1)Classifier fusion (1)Convex Relaxation (1)Ensemble (1)Feature selection (1)Genetic algorithms (1)Integral image (1)... View MoreDate Issued2018 (3)Has File(s)Yes (3)

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