Cost-sensitive learning for social network analysis and network measurement☆

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.