Abstract
Artificial Immune Systems (AISs) emerged in 1990s. The capability of AISs for learning new information, recalling what has been learned and recognizing a decentralized pattern are reasons why numerous models have been developed, implemented and used in various types of problems. This paper describes the implementation of AISs in solving an image classification problem. The Clonal Selection Algorithm has been chosen to evolve solutions in the form of antibodies during the recognizing process. In this approach, three types of shape were treated as antigens. Two experiments have been undertaken and the results show that the algorithm can perform with an accuracy of up to 95%. As a conclusion, the study showed that the Clonal Selection Algorithm can be applied to shape recognition problems. ©2010 IEEE.
Original language | English |
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Title of host publication | Proceedings - 2010 International Conference on Information Retrieval and Knowledge Management: Exploring the Invisible World, CAMP'10 |
Pages | 223-228 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2010 |
Event | International Conference on Information Retrieval and Knowledge Management: Exploring the Invisible World, CAMP'10 - Shah Alam, Malaysia Duration: 17 Mar 2010 → 18 Mar 2010 |
Conference
Conference | International Conference on Information Retrieval and Knowledge Management: Exploring the Invisible World, CAMP'10 |
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Country/Territory | Malaysia |
City | Shah Alam |
Period | 17/03/10 → 18/03/10 |
Keywords
- AIS
- Artificial immune system
- Clonal selection
- CLONALG
- Recognition
- Shape classification