Artificial Bee Colony–Based Feature Selection Algorithm for Cyberbullying

Essiz, Esra Sarac; Oturakci, Murat

doi:10.1093/comjnl/bxaa066

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…They explored the effects of the ABC-based feature selection algorithm [26] to eliminates non-informative features on the classification performance of a cyberbully detection problem. They also showed that their method has a better performance than some conventional methods such as information gain, Relief, and chi-square [27].…”

Section: B Abc Applicationsmentioning

confidence: 98%

HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

Zahedi¹,

Mohammadi²,

Amini³

2021

Preprint

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Section: B Abc Applicationsmentioning

confidence: 98%

HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

Zahedi¹,

Mohammadi²,

Amini³

2021

Preprint

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“…To identify cyber-violence from sexual abusers via the internet, Sadiq et al [9] applied the methods of text analysis and communication theory. Sarac Essiz et al [10] used an extension of the least-square support vector machine (SVM) to identify online spam assaults in social media. To assist online harassment, Gomez et al [11] applied traditional machine learning methods for text classification.…”

Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

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Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

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“…An Evolutionary Algorithm (EA) is a computational method that solves problems by mimicking the behaviour of living organisms using nature-inspired mechanisms 21 . The use of EAs for feature selection has received significant attention in academia, with various algorithms being proposed, including Particle Swarm Optimization (PSO) [22][23][24] , Genetic Algorithm (GA) 25,26 , Artificial Bee Colony (ABC) 27 , Genetic Programming (GP) 28 , Gravitational Search Algorithm (GSA) 29 and Ant Colony Optimization (ACO) 30,31 . One advantage of EAs is their population-based search approach, which involves a team of entities exploring the fitness landscape to find the globally optimum solution.…”

Section: B Evolutionary Algorithmsmentioning

confidence: 99%

Bird's Eye View Feature Selection for High-Dimensional Data

Belhaouari

Shakeel

Erbad

et al. 2023

Preprint

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In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird's Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

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Artificial Bee Colony–Based Feature Selection Algorithm for Cyberbullying

Cited by 16 publications

References 20 publications

HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Bird's Eye View Feature Selection for High-Dimensional Data

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