A Hybrid Heuristic Dimensionality Reduction Methods for Classifying Malaria Vector Gene Expression Data
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Date
2020-10-02
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Access
Abstract
Malaria is the world’s leading cause of death, spread by Anopheles mosquitoes. Gene expression is a fundamental level where the effects of unseen vital revealing genes and developmental systems
can be evident for detection of distinctions in malaria infections, to recognize the biological processes in
human. Ribonucleic acid sequencing offers a large-scale measurable generated profiling transcriptional data
results that help a variety of applications such as scientific and clinical condition studies. A fundamental
limitation of ribonucleic acid sequencing consists of high dimensional, infrequent and noises, making
classification of genes challenging. Several approaches have proposed enhancing the problem of the curse
of dimensionality problem, requiring more improvement, yet it is critical to obtain accurate results. In this
study, a hybrid dimensionality reduction technique proposes an optimized Genetic algorithm to pick pertinent
subset features from the data. Features chosen is passed into principal component analysis and independent
component analysis methods grounded on their class variants, to help transform the selected elements into
a lower dimension separately. Support vector machine kernel classifiers used the reduced malaria vector
dataset to assess the classification performance of the experiment
Description
Keywords
Genetic algorithm, principal component analysis, independent component analysis, support vector machine, hybrid approach, RNA-sequencing, Nigeria, ACE: Applied Informatics and Communication, Covenant University, Digital Development