Predicting forest cover types with immune and genetic

Vegetation prediction is a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, and etc. In order to enhance the predicting accuracy, a novel method with immune and genetic to predict vegetation types, is presented. Based on immune genetic theory, the algorithm has two indexes: affinity and fitness, which was as the basis of antibody cloning to select the next generation. The more similarity of the structure among the artificial immune cells, the greater their affinity is, and the fitness calculation makes the population dynamic evolution for better, is good for the convergence of the antibody population. The algorithm has a better diversity, robustness, self-learning and adaptive capacity. It will provide a new solution for vegetation prediction. Experimental results of simulation demonstrate that this method has higher predicting accuracy than other methods for the same dataset. The algorithm is a better recognition solution for vegetation types.