國家衛生研究院 NHRI:Item 3990099045/5052
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    题名: The data complexity index to construct an efficient cross-validation method
    作者: Li, DC;Fang, YH;Fang, YMF
    贡献者: Division of Biostatistics and Bioinformatics
    摘要: Cross-validation is a widely used model evaluation method in data mining applications. However, it usually takes a lot of effort to determine the appropriate parameter values, such as training data size and the number of experiment runs, to implement a validated evaluation. This study develops an efficient cross-validation method called Complexity-based Efficient (CBE) cross-validation for binary classification problems. CBE cross-validation establishes a complexity index, called the CBE index, by exploring the geometric structure and noise of data. The CBE index is used to calculate the optimal training data size and the number of experiment runs to reduce model evaluation time when dealing with computationally expensive classification data sets. A simulated and three real data sets are employed to validate the performance of the proposed method in the study, while the validation methods compared are repeated random sub-sampling validation and K-fold cross-validation. The results show that CBE cross-validation, repeated random sub-sampling validation and K-fold cross-validation have similar validation performance, except that the training time required for CBE cross-validation is indeed lower than that for the other two methods.
    日期: 2010-12
    關聯: Decision Support Systems. 2010 Dec;50(1):93-102.
    Link to: http://dx.doi.org/10.1016/j.dss.2010.07.005
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0167-9236&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000284654800008
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=78049484286
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