This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the normal class via co-evolutionary genetic algorithm, which is inspired from the positive selection, and then generates synthetic samples of the anomaly class, which is based on the negative selection in the immune system. Two algorithms about synthetic generation of the anomaly class are proposed. One deals with data sets containing a few anomalous samples; while the other deals with data sets containing no anomalous samples at all. The experimental results on some benchmark data sets from UCI data set repertory show that the detection rate is improved evidently, accompanied by a slight increase in false alarm rate via introducing novel synthetic samples of the anomaly class. The advantages of our method are the increased ability of classifiers in identifying both previously known and innovative anomalies, and the maximal degradation of overfitting phenomenon.

The human immune system provides inspiration for solving a wide range of innovative problems. In this paper, we propse an immune network based approach for web document clustering. All the immune cells in the network competitively recognize the antigens (web documents) which are presented to the network one by one. The interaction between immune cells and an antigen leads to an augment of the network through the clonal selection and somatic mutation of the stimulated immune cells, while the interaction among immune cells results in a network compression. The structure of the immune network is well maintained by learning and self-regularity. We use a public web document data set to test the effectiveness of our method and compare it with other approaches. The experimental results demonstrate that the most striking advantage of immune-based data clustering is its adaptation in dynamic environment and the capability of finding new clusters automatically.

To avoid low quality problems caused by low quality data, the paper introduces an inexact field learning approach which derives rules by working on the fields of attributes with respect to classes, rather than on individual point values of attributes. The experimental results show that field learning achieved a higher prediction accuracy rate on new unseen test cases which is particularly true when the learning is performed on large low quality data.