Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier
Abstract RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-02-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-021-00415-z |
| _version_ | 1818459649931739136 |
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| author | Micheal Olaolu Arowolo Marion Olubunmi Adebiyi Ayodele Ariyo Adebiyi Oludayo Olugbara |
| author_facet | Micheal Olaolu Arowolo Marion Olubunmi Adebiyi Ayodele Ariyo Adebiyi Oludayo Olugbara |
| author_sort | Micheal Olaolu Arowolo |
| collection | DOAJ |
| description | Abstract RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is capable of adding to prevailing machine learning methods. |
| first_indexed | 2024-12-14T23:17:43Z |
| format | Article |
| id | doaj.art-1347e12670854637ba6e72d7a1f48338 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-14T23:17:43Z |
| publishDate | 2021-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-1347e12670854637ba6e72d7a1f483382022-12-21T22:44:02ZengSpringerOpenJournal of Big Data2196-11152021-02-018111410.1186/s40537-021-00415-zOptimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifierMicheal Olaolu Arowolo0Marion Olubunmi Adebiyi1Ayodele Ariyo Adebiyi2Oludayo Olugbara3Department of Computer Science, Landmark UniversityDepartment of Computer Science, Landmark UniversityDepartment of Computer Science, Landmark UniversityComputer Science and Information Technology, Durban University of TechnologyAbstract RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is capable of adding to prevailing machine learning methods.https://doi.org/10.1186/s40537-021-00415-zClassificationDimensionality reductionHybridMalaria Vector |
| spellingShingle | Micheal Olaolu Arowolo Marion Olubunmi Adebiyi Ayodele Ariyo Adebiyi Oludayo Olugbara Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier Journal of Big Data Classification Dimensionality reduction Hybrid Malaria Vector |
| title | Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier |
| title_full | Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier |
| title_fullStr | Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier |
| title_full_unstemmed | Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier |
| title_short | Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier |
| title_sort | optimized hybrid investigative based dimensionality reduction methods for malaria vector using knn classifier |
| topic | Classification Dimensionality reduction Hybrid Malaria Vector |
| url | https://doi.org/10.1186/s40537-021-00415-z |
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