Binary Bat Algorithm for text feature selection in news events detection model using Markov clustering

Al-Dyani, Wafa Zubair; Ahmad, Farzana Kabir; Kamaruddin, Siti Sakira

doi:10.1080/23311916.2021.2010923

Cited by 6 publications

(11 citation statements)

References 51 publications

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“…Two main categories of ED models exist: New Event Detection (NED) for real-time data streams and Retrospective Event Detection (RED) for historical archives [6]. Recently, ED models for digital news have gained traction due to their ability to gather information on real-life events [2], [7]- [10]. These models can process news from various sources, including social media, which leads to more informative results compared to using a single source [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

“…These models can process news from various sources, including social media, which leads to more informative results compared to using a single source [11]- [14]. Researchers have developed ED models to handle the heterogeneity of news sources, which can vary in structure, style, language, and length [7], [8], [13], [14]. ED models rely on pre-processing news text data and converting it into a usable format.…”

Section: Introductionmentioning

confidence: 99%

“…ED models rely on pre-processing news text data and converting it into a usable format. This process can create high-dimensional feature spaces that contain irrelevant information that affect the performance of detection and confuse identification methods [7], [15], [16] [8]. To address this, Feature Selection (FS) techniques are employed to choose the most informative features.…”

Section: Introductionmentioning

confidence: 99%

“…In our earlier works, two novel FS methods named Binary Bat Algorithm-Markov Clustering (BBAMCL) [7] and Adaptive BBAMCL [8] have been developed to tackle the high dimensionality and fast convergence issue for BBA, respectively. However, both methods can struggle with pattern detection due to their reliance on the Markov Clustering (MCL) algorithm [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

Al-Dyani

2024

IEEE Access

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Several Event Detection (ED) applications utilize various wrapper Feature Selection (FS) techniques based on wrapping the Markov Clustering Algorithm (MCL) with the Binary Bat Algorithm (BBA) or Adaptive Bat Algorithm (ABBA). These approaches have shown promising results in identifying relevant feature subset for MCL, leading to more precise event cluster from heterogeneous news articles. However, such wrapped FS methods involve coupling two methods (FS and ED) within ED model, with their performance influencing each other. While ABBA improved upon BBA's limitations, MCL's rapid convergence can hinder detection effectiveness. This fast convergence can lead to local optima and the detection of meaningless clusters. Additionally, MCL's identification ability diminishes as the feature space grows. To address these issues, this paper develops two novel adaptive techniques to control MCL's inflation (inf) and pruning (p) parameters, thereby managing its convergence behavior. Consequently, a new variant called Adaptive MCL (AMCL) is introduced and combined with ABBA. The effectiveness of the ABBA-AMCL method is evaluated using 10 benchmark datasets and two substantial Facebook news datasets. Various performance measures are employed to compare ABBA-AMCL against established methods. The empirical results demonstrate that ABBA-AMCL excels at extracting high-quality, real-world event clusters from various news text sources.INDEX TERMS Adaptive BBA, Markov Clustering (MCL), Event Detection (ED) methods, wrapper methods, heterogeneous news, adaptive techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

Al-Dyani

2024

IEEE Access

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“…We analyze their models and believe the reason is their training algorithms. After comparing recent global optimization algorithms, we find that particle swarm optimization (PSO) is one of the most successful optimization algorithms, compared to otheroptimization algorithms such as artificial bee colony [ 12 ] and bat algorithm [ 13 ]. Hence, we use the framework in Zhou [ 9 ] but replace CSO with an improved PSO.…”

Section: Introductionmentioning

confidence: 99%

WACPN: A Neural Network for Pneumonia Diagnosis

Wang¹,

Khan²,

Zhu³

et al. 2023

Computer Systems Science and Engineering

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Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87 ± 1.37%, a specificity of 90.70 ± 1.19%, a precision of 91.01 ± 1.12%, an accuracy of 91.29 ± 1.09%, F1 score of 91.43 ± 1.09%, an MCC of 82.59 ± 2.19%, and an FMI of 91.44 ± 1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment.

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Wrapper-based optimized feature selection using nature-inspired algorithms

Karlupia

Abrol

2023

Neural Comput & Applic

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Binary Bat Algorithm for text feature selection in news events detection model using Markov clustering

Cited by 6 publications

References 51 publications

Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

Adaptive Markov Clustering Algorithm for Optimal Event Detection From Heterogeneous News Documents

WACPN: A Neural Network for Pneumonia Diagnosis

Wrapper-based optimized feature selection using nature-inspired algorithms

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