id: 05805628
dt: a
an: 05805628
au: Ishii, Naohiro; Morioka, Yuichi; Kimura, Hiroaki; Bao, Yongguang
ti: Classification by multiple reducts-kNN with confidence.
so: Fyfe, Colin (ed.) et al., Intelligent data engineering and automated
    learning ‒ IDEAL 2010. 11th international conference, Paisley, UK,
    September 1‒3, 2010. Proceedings. Berlin: Springer (ISBN
    978-3-642-15380-8/pbk). Lecture Notes in Computer Science 6283, 94-101
    (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-15381-5_12
ab: Summary: Most classification studies are done by using all the objects
    data. It is expected to classify objects by using some subsets data in
    the total data. A rough set based reduct is a minimal subset of
    features, which has almost the same discernible power as the entire
    conditional features. Here, we propose multiple reducts with
    confidence, which are followed by the k-nearest neighbor to classify
    documents to improve the classification accuracy. To select better
    multiple reducts for the classification, we develop a greedy algorithm
    for the multiple reducts, which is based on the selection of useful
    attributes for the documents classification. These proposed methods are
    verified to be effective in the classification on benchmark datasets
    from the Reuters 21578 data set.
rv: