Privacy preserving rule-based classifier using modified artificial bee colony algorithm

Zorarpacı, Ezgi; Özel, Selma Ayşe

doi:10.1016/j.eswa.2021.115437

Cited by 16 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a mandatory step when the exclusion criteria may not filter irrelevant studies from this review. For example, metaheuristic-based algorithms have been proposed for feature selection [16], [17] and rule classification [18], [19]. Thus, the results of this step included 59 candidate-related studies.…”

Section: ) Full Paper Scanmentioning

confidence: 99%

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Aljehani,

Alotaibi

2024

IEEE Access

View full text Add to dashboard Cite

The current state of Association Rule Mining (ARM) technology is heading towards a critical yet profitable direction. The ARM process uncovers numerous association rules, determining correlations between itemsets, forming building blocks that have led to revolutionary scientific discoveries. However, a high level of privacy is vital for protecting sensitive rules, raising privacy concerns. Researchers recently highlighted challenges in the Privacy-Preserving Association Rule Mining (PPARM) field. Many studies have proposed workaround for the PPARM dilemma by using metaheuristics. This paper conducts a systematic literature review on metaheuristic-based algorithms addressing PPARM challenges. It explores existing studies, providing insights into diverse metaheuristic approaches tackling PPARM problems. A detailed taxonomy is presented, providing a structured classification of metaheuristic-based algorithms specific to PPARM. This classification facilitates a nuanced understanding of the field by categorizing these algorithms into metaphor-based and non-metaphor-based groups with discussion of the nature of the representation schemes for each category identified in the survey. This review extends its analysis to encompass the latest applied approaches, highlighting the diversification of existing metaheuristic algorithms in the PPARM context. Moreover, common datasets and evaluation metrics identified from selected studies are documented to provide a deeper understanding of the methodological choices made by researchers in this domain. Finally, a discussion of existing challenges and potential future directions are presented. This review serves as a helpful guide that outlines previous research and presents potential future opportunities of metaheuristic-based algorithms in the context of PPARM.

show abstract

Section: ) Full Paper Scanmentioning

confidence: 99%

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Aljehani,

Alotaibi

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Compared with the traditional optimization method, the swarm intelligence algorithm is independent of the initial value, gradient information, low function requirements, and good solution performance. Currently, the most common swarm intelligence algorithms include the ant colony algorithm (ACO) [48], artificial bee colony algorithm (ABC) [49], and glowworm swarm optimization (GSO) [50]. With the rise of meta-heuristic algorithms and the improvement in servo system control accuracy in recent years, many scholars have turned to meta-heuristic algorithms to identify friction model parameters.…”

Section: Metaheuristicsmentioning

confidence: 99%

A Review of Key Technologies for Friction Nonlinearity in an Electro-Hydraulic Servo System

et al. 2022

View full text Add to dashboard Cite

In a high-precision servo system, the nonlinear friction link is the key factor affecting the system performance. Reasonable solving of the friction link in servo systems has become a focus of current research. This paper summarizes the friction nonlinearity that affects the control performance of servo systems. First, the characteristics of friction are summarized, and the advantages and disadvantages of typical friction models in recent years are analyzed. Subsequently, existing friction model parameter identification methods are introduced and evaluated. On this basis, the development level of the friction nonlinear control strategy is analyzed from three aspects: friction model-based control, friction model-free control, and compound control. Finally, the objective advantages and disadvantages of the existing technology are summarized, and the future development direction of the friction model and selection reference for the nonlinear friction control strategy are comprehensively discussed.

show abstract

“…Over the last few decades, inspired by different natural phenomena, lots of nature-inspired algorithms have been developed. These algorithms have received considerable attention because of their derivative-free search mechanisms and excellent optimization capabilities (Benaissa et al, 2021;Khatir et al, 2021a), and many of them are extensively used nowadays including the particle swarm optimization (PSO) (Wei et al, 2020), ant colony optimization (ACO) (Guan et al, 2021), genetic algorithm (GA) (Chen et al, 2021), artificial bee colony (ABC) (Zorarpacı and Ayşe Özel, 2021), grey wolf optimizer (GWO) (Gupta and Deep, 2019), differential evolution (DE) (Houssein et al, 2022), atom search optimization (ASO) (Khatir et al, 2021b), cuckoo search (CS) (Li et al, 2021;Salgotra et al, 2021;Swain et al, 2022), and many others. CS, developed initially by Yang and Deb (2009), is becoming increasingly popular because of its powerful capability to deal with multifarious types of optimization problems (Abdel-Basset et al, 2017;Binh et al, 2018;Mohamad et al, 2014;Yang and Deb, 2014).…”

Section: Introductionmentioning

confidence: 99%

A collaborative cuckoo search algorithm with modified operation mode

Yang

Huang

Zhang

et al. 2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Privacy preserving rule-based classifier using modified artificial bee colony algorithm

Cited by 16 publications

References 42 publications

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

A Review of Key Technologies for Friction Nonlinearity in an Electro-Hydraulic Servo System

A collaborative cuckoo search algorithm with modified operation mode

Contact Info

Product

Resources

About