Third International Conference on Information Technology: New Generations (ITNG'06) Feature and Prototype Evolution for Nearest Neighbor Classification of Web Documents Las Vegas, Nevada April 10-April 12 ISBN: 0-7695-2497-4
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ITNG.2006.64
A Nearest Neighbor Classifier (NNC) approaches the problem of text classification by computing a similarity metric between feature vector representations of an unknown document and a set of known prototype documents. The accuracy and speed of the NNC are dependent upon the choices of features and prototypes. In this work we consider the use of a genetic algorithm to optimize the feature and prototype sets for an NNC. We also examine whether simultaneously evolving the feature and prototype sets produces better results than sequential optimization.
Citation:
Michelle Cheatham, Mateen Rizki, "Feature and Prototype Evolution for Nearest Neighbor Classification of Web Documents," itng, pp.364-369, Third International Conference on Information Technology: New Generations (ITNG'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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