Adaptive Binary Bat and Markov Clustering Algorithms for Optimal Text Feature Selection in News Events Detection Model

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

doi:10.1109/access.2022.3198654

Cited by 4 publications

(13 citation statements)

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“…The established method, that forms the core foundation of this study, is a combination of the two algorithms: ABBA [8] and Adaptive Markov Clustering (AMCL), which is an upgrade version of the basic MCL algorithm. More information regarding these methods is presented in the following sections.…”

Section: Methodsmentioning

confidence: 99%

“…ABBA-MCL [8] is a recently developed wrapper FS method that integrates ABBA with the basic MCL. It has been used to identify the optimal features from a highdimensional feature space generated by a huge volume of heterogeneous news text articles.…”

Section: A Adaptive Binary Bat Algorithm (Abba)mentioning

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: Methodsmentioning

confidence: 99%

Section: A Adaptive Binary Bat Algorithm (Abba)mentioning

confidence: 99%