Novel hybrid classification model for multi-class imbalanced lithology dataset

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dc.contributor.authorAlyasin, Eman Ibrahim
dc.contributor.authorAta, Oğuz
dc.contributor.authorMohammedqasim, Hayder
dc.date.accessioned2022-10-21T14:03:59Z
dc.date.available2022-10-21T14:03:59Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractDeep learning methods and applications assist geologists in predicting and identifying lithologies in different surveys, hence lowering operational costs and uptime. This allows accurate data analysis and completion of scientific research on data obtained at geologically different places. This study used 4 lithologies datasets with high dimensionality and multiclass imbalance problems for analysis and classification. The imbalance of data classification is one of the most important problems facing current data analysis. Data imbalance can considerably influence classification performance, especially when dealing with other difficulty variables such as the presence of overlapping class distributions. This impact is especially obvious in multi-class conditions when mutual imbalance relations across classes complicate matters even further. Moreover, the problem of high dimensionality can lead to increased computing complexity and overfitting, and thus these issues can affect classification performance. To overcome these problems, we developed a new hybrid deep learning multi-class imbalanced learning method that combines Synthetic Minority Oversampling (SMOTE) to resample the data, and Recursive Feature Elimination (RFE) to identify the most useful predictive features. Finally, we believe that our developments can help improve geology research by providing accurate classification and rapid answers about interpreting data obtained in various study areas.en_US
dc.identifier.citationAlyasin, E. I., Ata, O., Mohammedqasim, H. (2022). Novel hybrid classification model for multi-class imbalanced lithology dataset. Optik, (270), 170047.en_US
dc.identifier.issn0030-4026
dc.identifier.issn1618-1336
dc.identifier.scopus2-s2.0-85139591923
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2985
dc.identifier.volume270en_US
dc.identifier.wosWOS:000875639300011
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlyasin, Eman Ibrahim
dc.institutionauthorAta, Oğuz
dc.institutionauthorMohammedqasim, Hayder
dc.language.isoen
dc.publisherElsevier GmbHen_US
dc.relation.ispartofOptik
dc.relation.isversionof10.1016/j.ijleo.2022.170047en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature Selectionen_US
dc.subjectHyperparameters Optimizationen_US
dc.subjectLithologyen_US
dc.subjectMulti-Class Imbalanced Learningen_US
dc.subjectSynthetic Minority Oversampling Techniqueen_US
dc.titleNovel hybrid classification model for multi-class imbalanced lithology dataset
dc.typeArticle

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