Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/3696
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Çelik, S. | - |
dc.contributor.author | Koyuncu, H. | - |
dc.date.accessioned | 2023-03-03T13:33:36Z | - |
dc.date.available | 2023-03-03T13:33:36Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 9781665475198 | - |
dc.identifier.uri | https://doi.org/10.1109/MAJICC56935.2022.9994150 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/3696 | - |
dc.description | 2022 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2022 -- 27 October 2022 through 28 October 2022 -- 185726 | en_US |
dc.description.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. | en_US |
dc.description.sponsorship | This work is supported by the Coordinatorship Technical University's Scientific Research Projects. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | Proceedings of the 2022 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2022 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Binarization | en_US |
dc.subject | Chaotic Behaviour | en_US |
dc.subject | Feature Selection | en_US |
dc.subject | Optimization | en_US |
dc.subject | Pattern Classification | en_US |
dc.subject | Wrapper Method | en_US |
dc.subject | Classification (of information) | en_US |
dc.subject | Nearest neighbor search | en_US |
dc.subject | Particle swarm optimization (PSO) | en_US |
dc.subject | Binarizations | en_US |
dc.subject | Binary conversion | en_US |
dc.subject | Chaotic behaviour | en_US |
dc.subject | Chaotic particle swarm optimizations | en_US |
dc.subject | Features selection | en_US |
dc.subject | Gauss maps | en_US |
dc.subject | Optimisations | en_US |
dc.subject | Optimization method | en_US |
dc.subject | Patterns classification | en_US |
dc.subject | Wrapper methods | en_US |
dc.subject | Feature Selection | en_US |
dc.title | Feature Selection via GM-CPSO and Binary Conversion: Analyses on a Binary-Class Dataset | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/MAJICC56935.2022.9994150 | - |
dc.identifier.scopus | 2-s2.0-85146436328 | en_US |
dc.department | KTUN | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57221816338 | - |
dc.authorscopusid | 55884277600 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Conference Object | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
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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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