A hybrid immune-estimation distribution of algorithm for mining thyroid gland data

In this paper we combine the main concepts of estimation of distribution algorithms (EDAs) and immune algorithms (IAs) to be a hybrid algorithm called immune-estimation of distribution algorithms (IEDA). Both EDAs and IAs are extended from genetic algorithms (GAs). EDAs eliminate the genetic operation including crossover and mutation from the GAs and places more emphasis on the relation between gene loci. It adopts the distribution of selected individuals in search space and models the probability distributions to generate the next population. However, the primary gap of EDAs is lock of diversity between individuals. Hence, we introduce the IAs that is a new branch in computational intelligence. The main concepts of IAs are suppression and hypermutation that make the individuals be more diversity. Moreover, the primary gap of IAs is to pay no attention to the relation between individuals. Therefore, we combine the main concepts of two algorithms to improve the gaps each other. The classification risk of data mining is applied by this paper and compares the results between IEDA and general GAs in the experiments. We adopt the thyroid gland data set from UCI databases. Based on the obtained results, our research absolute is better than general GAs including accuracy, type I error and type II error. The results show not only the excellence of accuracy but also the robustness of the proposed algorithm. In this paper we have got high quality results which can be used as reference for hospital decision making and research workers.