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An Immune System Approach to Document Classification
Keyword(s): artificial immune system; concept learning; classifier; machine learning; information retrieval; cooperative coevolution; feature extraction
Abstract: The human immune system as a biological complex adaptive system has recently provided inspiration for a range of innovative problem solving techniques in areas such as computer security, knowledge management and information retrieval. In this dissertation 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 tested in a rigorous and systematic manner, first on a standard classification problem and then, combined with an HTML feature extractor, on a web-based document classification task in the context of a system which allows users to perform document-based searches and automatically refine search results. The immune-based classifier is found to outperform traditional classification paradigms on both tasks. Further applications in community knowledge management systems, content filtering, recommendation systems and user profile generation are also directly relevant to the work presented.
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