Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

Watkins, Andrew; Timmis, Jon; Boggess, Lois

doi:10.1023/b:genp.0000030197.83685.94

Cited by 319 publications

(180 citation statements)

References 19 publications

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“…The classifier system was named AIRS and it is based on the principle of resourcelimited AIS and made use of artificial recognition balls. AIRS has proved to be a very powerful classification tool and when compared to the 30 best classifiers on publicly available classification problem sets, one of which is a credit scoring dataset, it was found to be among the top five to eight classifiers for every problem set, except for one in which it ranked second [8].…”

Section: Artificial Immune Systems (Ais)mentioning

confidence: 99%

“…Even though AIS has been used in the area of pattern recognition and classification, there has only been a single case where it has been applied to credit scoring purposes. Watkins et al [8] found that their AIS, known as artificial immune recognition system (AIRS), exhibited the best performance of any single classifier used on their dataset.…”

Section: Introductionmentioning

confidence: 99%

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Consumer credit scoring using an artificial immune system algorithm

Leung

Cheong

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Credit scoring has become a very important task in the credit industry and its use has increased at a phenomenal speed through the mass issue of credit cards since the 1960s. This paper compares the performance of current classifiers against an artificial intelligence technique based on the natural immune system, named simple artificial immune system (SAIS). Experiments were performed on three benchmark credit datasets and SAIS was found to be a very competitive classifier.

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Section: Artificial Immune Systems (Ais)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consumer credit scoring using an artificial immune system algorithm

Leung

Cheong

2007

2007 IEEE Congress on Evolutionary Computation

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“…The model belongs to the class of k-nearest-neighbors algorithms. Its main features, compared to other artificial immune classifiers [5,6,7,8,9,10,11,12,13,14,15,16,13,17,18,19,20,21,22], are the assumption that the training set constitutes the initial antibodies' population of the system, and a suppression mechanism that tries to reduce this training set into a smaller subset. This subset is supposed to contain the most significative samples, without loosing much capability of generalization.…”

Section: Introductionmentioning

confidence: 99%

The SUPRAIC Algorithm: A Suppression Immune Based Mechanism to Find a Representative Training Set in Data Classification Tasks

Figueredo

Ebecken

Barbosa

2007

Lecture Notes in Computer Science

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Abstract. This article proposes a new classifier inspired on a biological immune systems' characteristic. This immune based predictor also belongs to the class of k-nearest-neighbors algorithms. Nevertheless, its main features, compared to other artificial immune classifiers, are the assumption that training set is the antibodies' population and a suppression mechanism that tries to reduce the training set into a smaller subset. This subset is supposed to contain the most significative samples, without loosing much capability of generalization. It is known that in prediction problems, the choice of a good training set is crucial for the classification process. And this is the focus of this research. Experiments using some benchmarks and the analysis of the results of our ongoing work are presented.

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“…The systems selected for this comparison are a Hybrid-Neural approach and, for AIS, Artificial Immune Recognition Systems (AIRS, [11]) and aiNet [12]. All of these systems have been described in the following sections of this paper.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Systems and Artificial Immune Systems: Performances and Applications to Biomedical Research

Bevilacqua

Musso

Menolascina

et al. 2007

Advances in Neural Networks – ISNN 2007

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Abstract. In this paper we propose a comparative study of Artificial Neural Networks (ANN) and Artificial Immune Systems. Artificial Immune Systems (AIS) represent a novel paradigm in the field of computational intelligence based on the mechanisms that allow vertebrate immune systems to face attacks from foreign agents (called antigens). Several similarities as well as differences have been shown by Dasgupta in [1]. Here we present a comparative study of these two approaches considering evolutions of the concepts of ANN and AIS, respectively hybrid neural systems, Artificial Immune Recognition Systems (AIRS) and aiNet. We tried to establish a comparison among these three methods using a well known dataset, namely the Wisconsin Breast Cancer Database. We observed interesting trends in systems' performances and capabilities. Peculiarities of these systems have been analyzed, possible strength points and ideal contexts of application suggested. These and other considerations will be addressed in the rest of this manuscript.

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Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 319 publications

References 19 publications

Consumer credit scoring using an artificial immune system algorithm

Consumer credit scoring using an artificial immune system algorithm

The SUPRAIC Algorithm: A Suppression Immune Based Mechanism to Find a Representative Training Set in Data Classification Tasks

Hybrid Systems and Artificial Immune Systems: Performances and Applications to Biomedical Research

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