Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/3696
Title: | Feature Selection via GM-CPSO and Binary Conversion: Analyses on a Binary-Class Dataset | Authors: | Çelik, S. Koyuncu, H. |
Keywords: | Binarization Chaotic Behaviour Feature Selection Optimization Pattern Classification Wrapper Method Classification (of information) Nearest neighbor search Particle swarm optimization (PSO) Binarizations Binary conversion Chaotic behaviour Chaotic particle swarm optimizations Features selection Gauss maps Optimisations Optimization method Patterns classification Wrapper methods Feature Selection |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | Feature selection is oft-used to upgrade the system performance in classification-based applications. For this purpose, wrapper-based methods reserve an important place and are designed with efficient optimization methods so as to observe the highest performance. In this paper, a state-of-the-art optimization method named Gauss map-based chaotic particle swarm optimization (GM-CPSO) is handled. Binary conversion is considered to adapt the GM-CPSO to the feature selection. In classification part of the proposed method, k-nearest neighborhood (k-NN) is operated due to its fast and robust performance on classification-based implementations. In experiments, seven metrics (accuracy, sensitivity, specificity, g-mean, precision, f-measure, AUC) are utilized to objectively evaluate the performances, and 80%/20% training-test split is fulfilled to effectively assign the necessary features. Our wrapper-based method is tested on a balanced dataset that is based on Parkinson's disease (PD). As a result, our method presents promising scores by means of seven metrics, and especially, it improves the classification performance about 14.59% concerning the accuracy and AUC rates in comparison with the k-NN method. © 2022 IEEE. | Description: | 2022 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2022 -- 27 October 2022 through 28 October 2022 -- 185726 | URI: | https://doi.org/10.1109/MAJICC56935.2022.9994150 https://hdl.handle.net/20.500.13091/3696 |
ISBN: | 9781665475198 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
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Feature_Selection_via_GM-CPSO_and_Binary_Conversion_Analyses_on_a_Binary-Class_Dataset.pdf Until 2030-01-01 | 1.32 MB | Adobe PDF | View/Open Request a copy |
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