An Efficient PCA Ensemble Learning Approach for Prediction of RNA-Seq Malaria Vector Gene Expression Data Classification

Arowolo, Micheal Olaolu; Adebiyi, Marion O.; Adebiyi, Ayodele Ariyo

doi:10.37624/ijert/13.1.2020.163-169

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…It is a common, supportive task that gives and predicts class labels specified from the predefined class label to current data. The building of classification is comprised of two steps [43]:…”

Section: Classificationmentioning

confidence: 99%

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Arowolo

Adebiyi

et al. 2021

J Big Data

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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.

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Section: Classificationmentioning

confidence: 99%

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Arowolo

Adebiyi

et al. 2021

J Big Data

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“…Table 3 shows the comparison of this study with other techniques in literature. [29] 89 Mutual information+KNN [30] 95 GA+MLP [31] 89 RF [32] 94 Bayesian [33] 91…”

Section:  Issn: 2302-9285mentioning

confidence: 99%

A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

2021

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Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors. There are transcriptomes of a thousand distinct species. Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition. RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures. Scientists have suggested many addressed learning for the study of biological evidence. An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms. The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively.

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“…To make the processing much simple and fast, especially with large datasets, we used the Principal Component Analysis (PCA) method to reduce the dimension of the signature while maintaining up to 90% of its performance [57]. The central idea of principal component analysis is to reduce the dimensionality of a data set consisting of a large number of interrelated variables while retaining as much as possible of the variation present in the data set [58,59]. As a result, a two-dimensional representation was adopted as shown in Fig.…”

Section: Signature Optimizationmentioning

confidence: 99%

A distributed Content-Based Video Retrieval system for large datasets

Saoudi

Andaloussi

2021

J Big Data

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With the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video Retrieval (CBVR) is nowadays an active area of research. In this article, a CBVR system providing similar videos from a large multimedia dataset based on query video has been proposed. This approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key frames for rapid browsing and efficient video indexing. The proposed method has been implemented on both single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments were performed using various benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to previous studies.

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An Efficient PCA Ensemble Learning Approach for Prediction of RNA-Seq Malaria Vector Gene Expression Data Classification

Cited by 13 publications

References 23 publications

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

A distributed Content-Based Video Retrieval system for large datasets

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