Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree

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


Official URL: https://www.ukm.my/jsm/malay_journals/jilid50bil9_...


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.

Item Type:Article
Keywords:Decision tree; Independent component analysis; Malaria vector; Optimized genetic algorithm; Principal component analysis
Journal:Sains Malaysiana
ID Code:18056
Deposited By: ms aida -
Deposited On:14 Feb 2022 06:50
Last Modified:18 Feb 2022 00:41

Repository Staff Only: item control page