An Improved Text Feature Selection for Clustering Using Binary Grey Wolf Optimizer

Abasi, Ammar Kamal; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Naim, Syibrah; Makhadmeh, Sharif Naser; Alyasseri, Zaid Abdi Alkareem

doi:10.1007/978-981-15-5281-6_34

Cited by 22 publications

(11 citation statements)

References 27 publications

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“…Typically, these methods use each cluster‐centroid to attract similar data (Abasi, Khader, Al‐Betar, Naim, & Alyasseri, et al, 2020). The ultimate aim of these methods is to efficiently disperse a massive amount of data into a collection of heterogeneous clusters, each with homogeneous data (Abasi et al, 2021). Figure 7a shows example of agglomerative and divisive hierarchical clustering to a dataset of five clusters, { a , b , c , d , e } and Figure 7b shows structure of partition clustering to a dataset of fourteen clusters.…”

Section: Machine Learning and Deep Learning For Covid‐19mentioning

confidence: 99%

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

et al. 2021

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COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development.

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Section: Machine Learning and Deep Learning For Covid‐19mentioning

confidence: 99%

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

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“…Advanced GWO (ABGWO) applied to twelve datasets of UCI and showed superior results as compared to other algorithms. There are several versions of GWO are developed for classification in different fields such as medical diagnosis [177], cervical cancer [178], electromyography (EMG) signal [179], facial emotion recognition [180], text feature selection [181] etc.…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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“…Although TAs can deeply search the search space region of the initial solution and reach the local optima, they fail to navigate some search space regions simultaneously. The main TAs, which are utilized for TDC, include K-means and K-medoids, whereas other TAs, which are used for TDC, include β-hill climbing (Abasi et al, 2019) and self-organizing maps (SOM). SI represents the natural metaheuristics group, which is inspired by 'swarms collective intelligence'.…”

Section: Introductionmentioning

confidence: 99%

“…The PSO (Abasi et al, 2020b) is developed following the birds' swarm behavior. The firefly algorithm (FA) (Abasi et al, 2019) is formulated according to the fireflies' flashing behavior, whereas the Bat Algorithm (BA) (Makhadmeh et al, 2020) depends on the bats' echolocation behavior.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Verse Optimizer for Text Documents Clustering

Abasi

Khader

Al-Betar³

2022

kje

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Text document clustering (TDC) represents a key task in text mining and unsupervised machine learning, which partitions a specific documents' collection into varied K-groups according to certain similarity/dissimilarity criterion. The multi-verse optimizer algorithm (MVO) is a stochastic population-based algorithm, which was recently introduced and successfully utilized to tackle many optimization problems that are complex. The original MVO performance is limited to the utilization of only the best solution in the exploitation phase (local search capability), which makes it suffer from entrapment in local optima and low convergence rate.This paper aims to propose a novel method of modifying the MVO algorithm called link-based multi-verse optimizer algorithm (LBMVO) to enhance the exploitation phase in the original MVO. Generally, LBMVO has outperformed or at least showed that it is profoundly competitive compared with the original MVO algorithm.

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An Improved Text Feature Selection for Clustering Using Binary Grey Wolf Optimizer

Cited by 22 publications

References 27 publications

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

An Improved Multi-Verse Optimizer for Text Documents Clustering

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