research-article

An evolutionary data-conscious artificial immune recognition system

Authors:

Richard KitneyAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 1101 - 1108

https://doi.org/10.1145/2463372.2463499

Published: 06 July 2013 Publication History

Abstract

Artificial Immune Recognition System (AIRS) algorithm offers a promising methodology for data classification. It is an immune-inspired supervised learning algorithm that works efficiently and has shown comparable performance with respect to other classifier algorithms. For this reason, it has received escalating interests in recent years. However, the full potential of the algorithm was yet unleashed.

We proposed a novel algorithm called the evolutionary data-conscious AIRS (EDC-AIRS) algorithm that accentuates and capitalizes on 3 additional immune mechanisms observed from the natural immune system. These mechanisms are associated to the phenomena exhibited by the antibodies in response to the concentration, location and type of foreign antigens. Bio-mimicking these observations empower EDC-AIRS algorithm with the ability to robustly adapt to the different density, distribution and characteristics exhibited by each data class. This provides competitive advantages for the algorithm to better characterize and learn the underlying pattern of the data. Experiments on four widely used benchmarking datasets demonstrated promising results -- outperforming several state-of-the-art classification algorithms evaluated. This signifies the importance of integrating these immune mechanisms as part of the learning process.

References

[1]

A. Freitas, and J. Timmis, "Revisiting the Foundations of Artificial Immune Systems for Data Mining", IEEE Transactions on Evolutionary Computation, vol. 11, no. 4, pp. 521--540, 2007.

Digital Library

[2]

L. N. D. Castro, and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach. Springer, 2002, pp. 398.

Digital Library

[3]

A. Watkins, J. Timmis, and L. Boggess, "Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm", Genetic Programming and Evolvable Machines, vol. 5, no. 3, pp. 291--317, 2004.

Digital Library

[4]

J. Timmisa1, and M. Nealb, "A Resource Limited Artificial Immune System for Data Analysis", Knowledge-Based Systems, vol. 14, pp. 121--130, 2001.

Digital Library

[5]

A. Watkins and L. Boggess, "A New Classifier Based on Resource Limited Artificial Immune Systems", Proceedings of Congress on Evolutionary Computation, 2002, Honolulu, USA: 2002.

Digital Library

[6]

J. Brownlee, "Artificial Immune Recognition System (AIRS). A Review and Analysis", Technical Report No. 1-02, Centre for Intelligent Systems and Complex Processes (CISCP), Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology (SUT), Victoria, Australia: 2005

[7]

M. Moses and S. Banerjee, "Biologically Inspired Design Principles for Scalable, Robust, Adaptive, Decentralized Search and Automated Response (RADAR)", IEEE Symposium on Artificial Life (ALIFE), 2011.

[8]

J. Holland, "Genetic Algorithms", Sci Am, pp. 66--72, 1992.

[9]

A.J. Chipperfield and P.J. Fleming, "The MATLAB Genetic Algorithm Toolbox", IEE Colloquium on Applied Control Techniques Using MATLAB, 1995.

[10]

C. L. Huanga, and C. J. Wangb, "A GA-based Feature Selection and Parameters Optimization for Support Vector Machines", Expert Systems with Applications, vol. 31, no. 2, pp. 231--240, 2006.

[11]

A. Watkins, "AIRS: A Resource Limited Artificial Immune Classifier," Master's Thesis, Mississippi State University, United States, 2001.

[12]

C.L. Blake and C.J. Merz, "UCI Repository of Machine Learning Databases," Available at: http://www.ics.uci.edu/~mlearn/MLRepository.html. Accessed 8/11/2012.

[13]

L. Fried, N. Borhani, P. Enright, and C. Furberg et al., "The Cardiovascular Health Study: Design and Rationale", Ann Epidemiol., vol. 1, no. 3, pp. 263--276, 1991.

[14]

C. Brodley, "Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection", In Proc. 10th Machine Learning Conf., 1993.

[15]

R. Bellman, Adaptive Control Processes. Princeton New Jersey: Princeton University Press, 1961, pp. 274.

[16]

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth, "Occam's Razor", Information Processing Letters, vol. 24, no. 6, pp. 377--380, 1987.

Digital Library

Cited By

Ishwarya AJayanthi S(2020)Risk Prediction Analysis of Cardiovascular Disease Using Supervised Machine Learning TechniquesProceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications10.1007/978-3-030-24051-6_41(419-431)Online publication date: 13-Mar-2020
https://doi.org/10.1007/978-3-030-24051-6_41
Tay DPoh CKitney R(2015)A novel neural-inspired learning algorithm with application to clinical risk predictionJournal of Biomedical Informatics10.1016/j.jbi.2014.12.01454:C(305-314)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.jbi.2014.12.014

Index Terms

An evolutionary data-conscious artificial immune recognition system
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

Recommendations

Artificial Immune Recognition System for DNA Microarray Data Analysis
ICNC '08: Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 06

Artificial Immune Systems (AIS) are emerging information processing methods, which embody the principles of biological immune systems for tackling complex realworld problems. The Artificial Immune Recognition System (AIRS) is a new kind of supervised ...
An immune memory clonal algorithm for numerical and combinatorial optimization

Inspired by the clonal selection theory together with the immune network model, we present a new artificial immune algorithm named the immune memory clonal algorithm (IMCA). The clonal operator, inspired by the immune system, is discussed first. The ...
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO '13

Sponsor:

SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

Acceptance Rates

GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
145
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ishwarya AJayanthi S(2020)Risk Prediction Analysis of Cardiovascular Disease Using Supervised Machine Learning TechniquesProceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications10.1007/978-3-030-24051-6_41(419-431)Online publication date: 13-Mar-2020
https://doi.org/10.1007/978-3-030-24051-6_41
Tay DPoh CKitney R(2015)A novel neural-inspired learning algorithm with application to clinical risk predictionJournal of Biomedical Informatics10.1016/j.jbi.2014.12.01454:C(305-314)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.jbi.2014.12.014

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents