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An immune-based approach to document classification

Twycross, Jamie; Cayzer, Steve

HPL-2002-292

Keyword(s): artificial immune system; document classification; machine learning; concept learning; coevolution

Abstract: The human immune system as a biological complex adaptive system has provided inspiration for a range of innovative problem solving techniques in areas such as computer security, knowledge management and information retrieval. In this paper the construction and performance of a novel immune-based learning algorithm is explored whose distributed, dynamic and adaptive nature offers many potential advantages over more traditional models. Through a process of cooperative coevolution a classifier is generated which consists of a set of detectors whose local dynamics enable the system as a whole to group positive and negative examples of a concept. The immune-based learning algorithm is first validated on a standard dataset. Then, combined with an HTML feature extractor, it is tested on a web-based document classification task and found to outperform traditional classification paradigms. Further applications in document based searching, content filtering, recommendation systems and user profile generation are also directly relevant to the work presented.

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