A danger theory inspired learning model and its application to spam detection

Abstract

This paper proposes a Danger Theory (DT) based learning (DTL) model for combining classifiers. Mimicking the mechanism of DT, three main components of the DTL model, namely signal I, danger signal and danger zone, are well designed and implemented so as to define an immune based interaction between two grounding classifiers of the model. In addition, a self-trigger process is added to solve conflictions between the two grounding classifiers. The proposed DTL model is expected to present a more accuracy learning method by combining classifiers in a way inspired from DT. To illustrate the application prospects of the DTL model, we apply it to a typical learning problem - e-mail classification, and investigate its performance on four benchmark corpora using 10-fold cross validation. It is shown that the proposed DTL model can effectively promote the performance of the grounding classifiers. © 2011 Springer-Verlag.