Optimization and Application of Real-Valued Negative Selection Algorithm

Xu, Aiqiang; Liu, Yong; Xiuli, Zhao; Chunying, Yang; Li, Tingjun

doi:10.1016/j.proeng.2011.11.2496

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…The experimental results show that the detector generation rate of the CB-RNSA is higher than that of the conventional NSA. Aiqiang et al (2011) discovered that the problem of finding a good distribution of detectors in the NSA could be better handled by optimization. They proposed a new NSA using an optimization strategy based on a re-heating simulated annealing algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

Balogun¹,

Odeniyi²,

Omidiora³

et al. 2023

AIP

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Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system.

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Section: Literature Reviewmentioning

confidence: 99%