A Scalable Artificial Immune System Model for Dynamic Unsupervised Learning

Nasraoui, Olfa; González, Fabio A.; Cardona, Cesar; Rojas, Carlos; Dasgupta, Dipankar

doi:10.1007/3-540-45105-6_27

Cited by 27 publications

(13 citation statements)

References 7 publications

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“…gs1: (1,2,3,4,5,6,7,8,9) gs2: (4,2,1,3,7,9,6,5,8) Then, Transform according to the mapping. Apparently, 4,5,6,7 exchange with 3,7,9,6 directly; the x exchanges based on mapping relationship table, for example, in gs1, the third fig.…”

Section: B Algorithm Descriptionmentioning

confidence: 99%

“…is x (while the original one is 3), because the mapping(4,3), the present x becomes as 4. After such operation, the final variation sequence is: gs1´ (1,2,4,3,7,9,6,8,5) gs2´ (3,2,1,4,5,6,7,9,8) …”

Section: B Algorithm Descriptionmentioning

confidence: 99%

“…But the shortcoming is that computation is too much and the effect is not that good. In 1958, the Danish scholar Jerne proposed a mathematical model on the immune system as the first one [5]. From then on, the immune computation becomes a new area of artificial intelligence research.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Method for TSP Immune-Based

Jiang¹,

Wang²

2012

IJCCE

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Abstract-A model of TSP problem based on ArtificialImmune System is established. In the model, antibody cells and antigen cells was defined, and describe the dynamic process of memory cells to the problem. We refer to the conception of variation GA, and present GFE algorithm of evolution of superior genes, and make use of the principle of immune stability in the clone's procedure to evolve the set of antibody in order to get the global approximate solution rapidly and efficiently. The result of the experiment shows that the algorithm is not only feasible but also a comparative good method, which has a fast convergence speed and global optimization to solve the NP problems.

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Section: B Algorithm Descriptionmentioning

confidence: 99%

Section: B Algorithm Descriptionmentioning

confidence: 99%

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A Method for TSP Immune-Based

Jiang¹,

Wang²

2012

IJCCE

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“…Dynamic Weighted B-cell AIS (DWB): Nasraoui et al proposed the DWB AIS model which can also be applied to the clustering of non-stationary data [30,31,32]. An artificial lymphocyte is known as a dynamic weighted B-cell (DWB-cell) since each training pattern is grouped with all artificial lymphocytes to a certain degree of membership.…”

Section: Network Based Artificial Immune System Modelsmentioning

confidence: 99%

Clustering data in stationary environments with a local network neighborhood artificial immune system

Graaff

Engelbrecht

2011

Int. J. Mach. Learn. & Cyber.

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The network theory in immunology inspired the modeling of network based artificial immune system (AIS) models for data clustering. Current network based AIS models determine the network connectivity between artificial lymphocytes (ALCs) by measuring the spatial distance between these ALCs against a distance threshold or by grouping ALCs into sub-networks. This paper discusses alternative network topologies to determine the network connectivity between ALCs and the advantages of using these network topologies. The local network neighborhood AIS model is then proposed as a network based AIS model which uses an index-based ALC neighborhood to determine the network connectivity between ALCs. The proposed model is compared to existing network based AIS models which are applied to data clustering problems. Furthermore, a sensitivity analysis is also done on the proposed model to investigate the influence of the model's parameters on the quality of the clusters. The paper also gives a formal definition of data clustering and discusses the performance measures used to determine the quality of clusters.

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“…This may be attributed to the high computational costs arising from current AIN models [14] that render them unfit for remote-sensing image classification. There being too many user-defined parameters in current AIN models is another obstruction.…”

mentioning

confidence: 99%

A Supervised Artificial Immune Classifier for Remote-Sensing Imagery

Zhong

Zhang

Gong

et al. 2007

IEEE Trans. Geosci. Remote Sensing

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Abstract-The artificial immune network (AIN), which is a new computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to remote-sensing image classification has been rather limited. This paper presents a novel supervised classification algorithm based on a multiple-valued immune network, which is a novel AIN model, to perform remote-sensing image classification. The proposed method trains the immune network using the samples of regions of interest and obtains an immune network with memory to classify the remote-sensing imagery. Two experiments with different types of images are performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: Parallelepiped, Minimum Distance, Maximum Likelihood, and Back-Propagation Neural Network. The results evince that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and, hence, provides an effective option for processing remote-sensing imagery.

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A Scalable Artificial Immune System Model for Dynamic Unsupervised Learning

Cited by 27 publications

References 7 publications

A Method for TSP Immune-Based

A Method for TSP Immune-Based

Clustering data in stationary environments with a local network neighborhood artificial immune system

A Supervised Artificial Immune Classifier for Remote-Sensing Imagery

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