Pattern Recognition using Artificial Immune System
This work addresses pattern recognition for machine learning applications, but it appears incremental as it builds on existing artificial immune system methods.
The thesis tackled pattern recognition by proposing a new unsupervised classification algorithm called Unsupervised Clonal Selection Classification (UCSC), based on clonal selection principles, which is nearly parameter-free and adjusts itself for fast classification, with experiments showing it has good and reliable performance.
In this thesis, the uses of Artificial Immune Systems (AIS) in Machine learning is studded. the thesis focus on some of immune inspired algorithms such as clonal selection algorithm and artificial immune network. The effect of changing the algorithm parameter on its performance is studded. Then a new immune inspired algorithm for unsupervised classification is proposed. The new algorithm is based on clonal selection principle and named Unsupervised Clonal Selection Classification (UCSC). The new proposed algorithm is almost parameter free. The algorithm parameters are data driven and it adjusts itself to make the classification as fast as possible. The performance of UCSC is evaluated. The experiments show that the proposed UCSC algorithm has a good performance and more reliable.