Dimensionality reduction and representation for nearest neighbour learning
An increasing number of intelligent information agents employ Nearest Neighbour learning algorithms to provide personalised assistance to the user. This assistance may be in the form of recognising or locating documents that the user might find relevant or interesting. To achieve this, documents must be mapped into a representation that can be presented to the learning algorithm. Simple heuristic techniques are generally used to identify relevant terms from the documents. These terms are then used to construct large, sparse training vectors. The work presented here investigates an alternative representation based on sets of terms, called set-valued attributes, and proposes a new family of Nearest Neighbour learning algorithms that utilise this set-based representation. The importance of discarding irrelevant terms from the documents is then addressed, and this is generalised to examine the behaviour of the Nearest Neighbour learning algorithm with high dimensional data sets containing such values. A variety of selection techniques used by other machine learning and information retrieval systems are presented, and empirically evaluated within the context of a Nearest Neighbour framework. The thesis concludes with a discussion of ways in which attribute selection and dimensionality reduction techniques may be used to improve the selection of relevant attributes, and thus increase the reliability and predictive accuracy of the Nearest Neighbour learning algorithm.