id: 05280884
dt: a
an: 05280884
au: Chen, Jingnian; Huang, Houkuan; Tian, Fengzhan; Tian, Shengfeng
ti: A selective classifier for incomplete data.
so: Washio, Takashi (ed.) et al., Advances in knowledge discovery and data
    mining. 12th Pacific-Asia conference, PAKDD 2008 Osaka, Japan, May
    20‒23, 2008. Proceedings. Berlin: Springer (ISBN
    978-3-540-68124-3/pbk). Lecture Notes in Computer Science 5012. Lecture
    Notes in Artificial Intelligence, 870-876 (2008).
py: 2008
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-540-68125-0_86
ab: Summary: Classifiers based on feature selection (selective classifiers) are
    a kind of algorithms that can effectively improve the accuracy and
    efficiency of classification by deleting irrelevant or redundant
    attributes of a data set. Due to the complexity of processing
    incomplete data, however, most of them deal with complete data. Yet
    actual data are often incomplete and have many redundant or irrelevant
    attributes. So constructing selective classifiers for incomplete data
    is an important problem. With the analysis of main methods of
    processing incomplete data for classification, a selective classifier
    for incomplete data named RBSR (ReliefF algorithm-Based Selective
    Robust Bayes Classifier), which is based on the Robust Bayes
    Classifiers (RBC) and ReliefF algorithm, is presented. The proposed
    algorithm needs no assumptions about data sets that are necessary for
    previous methods of processing incomplete data in classification. This
    algorithm can deal with incomplete data sets with many attributes and
    instances. Experiments were performed on twelve benchmark incomplete
    data sets. We compared RBSR with the very effective RBC and several
    other classifiers for incomplete data. The experimental results show
    that RBSR can not only enormously reduce the number of redundant or
    irrelevant attributes, but greatly improve the accuracy and stability
    of classification as well.
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