Yazılım Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/2147
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Browsing Yazılım Mühendisliği Bölümü Yayın Koleksiyonu by Subject "Classification"
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Article Citation Count: Dokeroglu, Tansel; Deniz, Ayça; Kiziloz, Hakan E. (2022). "A comprehensive survey on recent metaheuristics for feature selection", Neurocomputing, Vol.494, pp.269-296.A comprehensive survey on recent metaheuristics for feature selection(2022) Dokeroglu, Tansel; Deniz, Ayça; Kiziloz, Hakan Ezgi; 234173Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.Article Citation Count: Dökeroğlu, Tansel. (2023). "A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients", Peerj Computer Science, Vol. 9.A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients(2023) Dökeroğlu, Tansel; 234173Harris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.Article Citation Count: Deniz, Ayça;...et.al. (2022). "Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm", Expert Systems, Vol.39, No.5.Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm(2022) Deniz, Ayça; Kızılöz, Hakan Ezgi; Sevinç, Ender; Dökeroğlu, Tansel; 234173The COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.
