Sains Malaysiana 50(9)(2021): 2579-2589

http://doi.org/10.17576/jsm-2021-5009-07

Enhanced Dimensionality Reduction Methods for Classifying Malaria Vector Dataset using Decision Tree

(Peningkatan Kaedah Pengurangan Kedimensian untuk Mengelaskan Set Data Vektor Malaria menggunakan Pokok Keputusan)

MICHEAL OLAOLU AROWOLO*, MARION OLUBUNMI ADEBIYI & AYODELE ARIYO ADEBIYI

Department of Computer Science, Landmark University, Omu-Aran, Nigeria

Received: 6 October 2020/Accepted: 21 January 2021

ABSTRACT

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.

Keywords: Decision tree; independent component analysis; malaria vector; optimized genetic algorithm; principal component analysis

ABSTRAK

Data RNA-Seq digunakan untuk aplikasi biologi dan membuat keputusan untuk pengelasan gen. Banyak kajian kebelakangan ini memfokus untuk mengurangkan dimensi data RNA-Seq. Pendekatan pengurangan dimensi telah diusulkan dalam pengambilan maklumat yang relevan dalam data yang diberikan. Dalam kajian ini, algoritma pengurangan dimensi optimum baharu dicadangkan dengan menggabungkan algoritma genetik yang dioptimumkan dengan Analisis Komponen Utama dan Analisis Komponen Bebas (GA-O-PCA dan GAO-ICA), yang digunakan untuk mengenal pasti ciri subset optimum dan korelasi laten. Pengelas menggunakan Pokok keputusan pada kumpulan data terturun nyamuk anopheles gambiae untuk meningkatkan ketepatan dan kebolehan pengukuran dalam analisis ekspresi gen. Algoritma yang dicadangkan digunakan untuk mengambil ciri yang relevan berdasarkan ruang ciri input dimensi tinggi. Ciri pemeringkatan dan pengalaman sebelumnya digunakan. Prestasi model dinilai dan disahkan menggunakan ketepatan pengelasan untuk membandingkan pendekatan sedia ada dalam kepustakaan. Hasil uji kaji yang dicapai terbukti menjanjikan ciri pemilihan dan pengelasan dalam analisis data ekspresi gen dan menentukan bahawa pendekatan tersebut merupakan pengumpulan yang mampu dilakukan terhadap teknik perlombongan data yang berlaku.

Kata kunci: Algoritma genetik yang dioptimumkan; analisis komponen bebas; analisis komponen utama; Pokok keputusan; vektor malaria

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*Corresponding author; email: arowolo.olaolu@lmu.edu.ng