V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage

Zhou, Ji; Dasgupta, Dipankar

doi:10.1016/j.ins.2008.12.015

Cited by 120 publications

(64 citation statements)

References 27 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As shown in Table 1, the time complexity of VorNSA is in logarithmic level with , which is much less than the traditional exponential level compared with NNSA [1], RNSA [2], and V-Detector [4].…”

Section: The Immune Detection Process Of Vornsa Undermentioning

confidence: 99%

See 1 more Smart Citation

A Quick Negative Selection Algorithm for One‐Class Classification in Big Data Era

Zhu

Chen

Han-li

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Negative selection algorithm (NSA) is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional "Random-Discard" model to the "Computing-Designated" model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.

show abstract

Section: The Immune Detection Process Of Vornsa Undermentioning

confidence: 99%

“…González et al presented the real-valued negative selection algorithm (RNSA) in 2003 [2], in which the detectors and antigens are studied in the real-value space. Ji and Dasgupta proposed V-Detector algorithm [3,4]. It turns the fixed-length detectors in RNSA into the variable-sized detectors to enlarge the detection areas.…”

Section: Introductionmentioning

confidence: 99%

A Quick Negative Selection Algorithm for One‐Class Classification in Big Data Era

Zhu

Chen

Han-li

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…A negative selection algorithm which supports real-valued representation is the constant sized negative selection algorithm (CRNS) [28]. This algorithm was extended to support some variable properties, like variable detector radius [29], resulting in the V-Detector. Next sections present a brief overview of CRNS and V-Detector.…”

Section: Negative Selectionmentioning

confidence: 99%

“…Several versions of V-Detector were proposed, as presented in [29]. In this paper, we adopted the boundary-aware version with statistical coverage estimation due to the improved performance seen in previous work.…”

Section: V-detectormentioning

confidence: 99%

See 1 more Smart Citation

Emphasizing typing signature in keystroke dynamics using immune algorithms

Pisani

Lorena

2015

Applied Soft Computing

View full text Add to dashboard Cite

Reliability‐based fault analysis models with industrial applications: A systematic literature review

Ahmed

Raza

Al-Anazi

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets‐intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state‐of‐the‐art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer‐reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators. A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co‐citation network analysis. Later, a detailed content analysis of the top‐100 most‐cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability‐based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset‐intensive industry, researchers, and policymakers.

show abstract

V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage

Cited by 120 publications

References 27 publications

A Quick Negative Selection Algorithm for One‐Class Classification in Big Data Era

A Quick Negative Selection Algorithm for One‐Class Classification in Big Data Era

Emphasizing typing signature in keystroke dynamics using immune algorithms

Reliability‐based fault analysis models with industrial applications: A systematic literature review

Contact Info

Product

Resources

About