Humoral artificial immune system (HAIS) For supervised learning

Nature over millions of years has found innovative, robust and effective methods through evolution for helping organisms deal with the challenges they face when attempting to survive in hostile and uncertain environments. Two critical natural mechanisms in this evolutionary process are variation and selection, which form the basis of `evolutionary computing´ (EC). EC has proved successful when dealing with complex problems, such as classification, clustering and optimization. In recent years, as our knowledge of microbiology has deepened, researchers have turned to microlevel biology for inspiration to help solve complex problems. This paper describes a novel supervised learning algorithm inspired from the humoral mediated response triggered by adaptive immune system. The proposed algorithm uses core immune system concepts such as memory cells, plasma cells and B-cells as well as parameters and processes inspired by our knowledge of the microbiology of immune systems, such as negative clonal selection and affinity thresholds. In particular, we show how local and global similarity based measures based on affinity threshold can help to avoid over-fitting data. The novelty of the proposed algorithm is discussed in the context of existing immune system based supervised learning algorithms. The performance of the proposed algorithm is tested on well known benchmarked real world datasets and the results indicate performance no worse than existing techniques in most cases and improvement over previous reported results in some.