Abstract
The k-nearest neighbor(k-NN) is improved by applying the distance functions with relearning and ensemble computations to classify text data with the higher accuracy values. The proposed relearning and combining ensemble computations are an effective technique for improving accuracy. We develop a new approach to combine kNN classifier based on weighted distance function with relearning and ensemble computations. The combining algorithm shows higher generalization accuracy, compared to other conventional algorithms. First, to improve classification accuracy, a relearning method with genetic algorithm is developed. Second, ensemble computations are followed by the relearning. Experiments have been conducted on some benchmark datasets from the UCI Machine Learning Repository.
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Ishii, N., Yamada, T., Bao, Y. (2008). Text Classification by Relearning and Ensemble Computation. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70560-4_18
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DOI: https://doi.org/10.1007/978-3-540-70560-4_18
Publisher Name: Springer, Berlin, Heidelberg
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