id: 05487624
dt: a
an: 05487624
au: Ishii, Naohiro; Yamada, Takahiro; Bao, Yongguang
ti: Improved accuracy by relearning and combining distance functions.
so: Lovrek, Ignac (ed.) et al., Knowledge-based intelligent information and
    engineering systems. 12th international conference, KES 2008, Zagreb,
    Croatia, September 3‒5, 2008. Proceedings, Part II. Berlin: Springer
    (ISBN 978-3-540-85564-4/pbk). Lecture Notes in Computer Science 5178.
    Lecture Notes in Artificial Intelligence, 926-933 (2008).
py: 2008
pu: Berlin: Springer
la: EN
cc: 
ut: text classification; distance functions for classification; relearning;
    ensemble computation
ci: 
li: doi:10.1007/978-3-540-85565-1_115
ab: Summary: The k-nearest neighbor (kNN) is improved by applying the distance
    functions with relearning and ensemble computations with the higher
    accuracy values. In this study, 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
    different distance functions with relearning and ensemble computations.
    The proposed combining algorithm shows higher generalization accuracy,
    compared to our previous studies and other conventional algorithms by
    artificial intelligence techniques. 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.
rv: