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- W2703937014 abstract "Abstract Recently, the application of data mining techniques to social network analysis and network measurement has received considerable attention. In this work, an aggregating N-dependence estimator (ANDE)-based cost-sensitive classification algorithm (CS_ANDE) was proposed for use with the unbalanced data commonly observed in social networks and network measurements. First, a one-dependence estimator was adopted to determine the approximate cost of misclassification. Second, multiple classifiers were constructed to minimize the misclassification cost. Subsequently, these classifiers were used to re-label samples. Ultimately, a CS_AODE classifier was obtained by learning these re-labeled samples. Consistent with two-dependence estimators, a CS_A2DE classifier was acquired. The CS_AODE and CS_A2DE classifiers were empirically evaluated against MetaCost and AODE for UCI datasets, and the results indicated that CS_AODE and CS_A2DE significantly outperformed the other classifiers and that the performance was stable under different parameters." @default.
- W2703937014 created "2017-06-30" @default.
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- W2703937014 date "2017-07-01" @default.
- W2703937014 modified "2023-09-22" @default.
- W2703937014 title "Cost-sensitive learning for social network analysis and network measurement" @default.
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- W2703937014 doi "https://doi.org/10.1016/j.compeleceng.2017.05.032" @default.
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