Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this s...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
|
| Online Access: | http://journalarticle.ukm.my/18056/ http://journalarticle.ukm.my/18056/1/7.pdf |
| _version_ | 1848814471678525440 |
|---|---|
| author | Arowolo, Micheal Olaolu Adebiyi, Marion Olubunmi Adebiyi, Ayodele Ariyo |
| author_facet | Arowolo, Micheal Olaolu Adebiyi, Marion Olubunmi Adebiyi, Ayodele Ariyo |
| author_sort | Arowolo, Micheal Olaolu |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining 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 Decision tree 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 from the high-dimensional input feature space. A feature ranking and earlier experience are used. 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 feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques. |
| first_indexed | 2025-11-15T00:34:37Z |
| format | Article |
| id | oai:generic.eprints.org:18056 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:34:37Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:180562022-02-18T00:41:18Z http://journalarticle.ukm.my/18056/ Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree Arowolo, Micheal Olaolu Adebiyi, Marion Olubunmi Adebiyi, Ayodele Ariyo RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining 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 Decision tree 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 from the high-dimensional input feature space. A feature ranking and earlier experience are used. 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 feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques. Penerbit Universiti Kebangsaan Malaysia 2021-09 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/18056/1/7.pdf Arowolo, Micheal Olaolu and Adebiyi, Marion Olubunmi and Adebiyi, Ayodele Ariyo (2021) Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree. Sains Malaysiana, 50 (9). pp. 2579-2589. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil9_2021/KandunganJilid50Bil9_2021.html |
| spellingShingle | Arowolo, Micheal Olaolu Adebiyi, Marion Olubunmi Adebiyi, Ayodele Ariyo Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree |
| title | Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree |
| title_full | Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree |
| title_fullStr | Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree |
| title_full_unstemmed | Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree |
| title_short | Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree |
| title_sort | enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree |
| url | http://journalarticle.ukm.my/18056/ http://journalarticle.ukm.my/18056/ http://journalarticle.ukm.my/18056/1/7.pdf |