An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data

Thaher, Thaer; Awad, Mohammed; Aldasht, Mohammed; Sheta, Alaa; Turabieh, Hamza; Chantar, Hamouda

doi:10.3897/jucs.78218

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…Feature selection plays a pivotal role in identifying and extracting the most informative features, thereby mitigating computational complexity and enhancing model interpretability [13].…”

Section: Introductionmentioning

confidence: 99%

Wrapper-based Modified Binary Particle Swarm Optimization for Dimensionality Reduction in Big Gene Expression Data Analytics

Salem,

Mead,

El-Taweel

2023

IJACSA

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Gene expression data has emerged as a crucial aspect of big data in genomics. The advent of high-throughput technologies such as microarrays and next-generation sequencing has enabled the generation of extensive gene expression data. These datasets are characterized by their complexity, fast data generation, diversity, and high dimensionality. Analyzing high dimensional gene expression data offers both challenges and opportunities. Computational intelligence and deep learning techniques have been employed to extract meaningful information from these enormous datasets. However, the challenges related to preprocessing, reducing dimensionality, and normalization continue to exist. This study explored the effectiveness of the Wrapper-based Modified Particle Swarm Optimization (WMBPSO) algorithm in reducing dimensionality of big gene expression data for Alzheimer's disease (AD) prediction, using the GSE33000 dataset. The reduced dataset was then used as input to a CNN-LSTM model for prediction. The WMBPSO method identified 4303 genes out of a total of 39280 genes as being relevant for AD. These genes were selected based on their discriminatory power and potential contribution to the classification task, achieving an accuracy score of 0.98. The performance of the CNN-LSTM model is evaluated using these selected genes, and the results were highly promising. The results of our analysis are 0.968 for mean cross-validation accuracy, 0.995 for AUC, and 0.967 for recall, precision, and F1 score. Importantly, our approach outperforms conventional feature selection methods and alternative machine and deep learning algorithms. By addressing the critical challenge of dimensionality reduction in gene expression data, our study contributes to advancing the field of AD prediction and underscores the potential for improved diagnosis and patient care.

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“…Feature selection plays a pivotal role in identifying and extracting the most informative features, thereby mitigating computational complexity and enhancing model interpretability [13].…”

Section: Introductionmentioning

confidence: 99%

Wrapper-based Modified Binary Particle Swarm Optimization for Dimensionality Reduction in Big Gene Expression Data Analytics

Salem,

Mead,

El-Taweel

2023

IJACSA

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Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Mafarja

Thaher

Al-Betar³

et al. 2023

Appl Intell

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Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms’ performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

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Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization

Thaher,

Sheta,

Awad

et al. 2024

Expert Systems with Applications

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An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data

Cited by 3 publications

References 48 publications

Wrapper-based Modified Binary Particle Swarm Optimization for Dimensionality Reduction in Big Gene Expression Data Analytics

Wrapper-based Modified Binary Particle Swarm Optimization for Dimensionality Reduction in Big Gene Expression Data Analytics

Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization

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