Classification with missing data using multi-layered artificial immune systems

The nature of missing data problems forces us to build models that maintain high accuracies and steadiness. The models developed to achieve this are usually complex and computationally expensive. In this paper, we propose an unsupervised multi-layered artificial immune system for an insurance classification problem that is characterised as highly dimensional and contains escalating missing data. The system is compared with the k-nearest neighbour, support vector machines and logistic discriminant models. Overall, the results show that whilst k-nearest neighbour achieves the highest accuracy, the multi-layered artificial immune system is steady and maintains high performance compared to other models, regardless of how the missing data is distributed in a dataset.