The present study drew from the social identity theory to explore the workplace friendship and adopted the social support theory to examine the effects of workplace friendship on affective commitment, helping behavior, as well as turnover intention. Research subjects of this study were civil affairs workers in Tainan and Chiayi County, Taiwan. Random sampling was used to collect anonymous questionnaires. The results of structural equation modeling (SEM) demonstrated that workplace friendship had positive influences on affective commitment and helping behavior and a negative influence on turnover intention. Prior research offered little empirical evidence of affective commitment as a mediating mechanism linking the workplace friendship–helping behavior and workplace friendship–turnover intention relationships. The present study found that effective commitment played an important mediating role. Implications for practice were discussed, and directions for future research were provided.

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Among many machine learning applications, classification is one of the important tasks. Most classification algorithms have been designed under the assumption that the number of samples for each class is approximately balanced. However, if the conventional classification approaches are applied to a class imbalanced dataset, it is likely to cause misclassification and, as a result, may distort classification performance results. Thus, in this study, we consider imbalanced classification problems and adopt an efficient preprocessing technique to improve the classification performances. In particular, we focus on borderline noise and outlier samples that belong to the majority class since they may influence classification performance. For this, we propose a hybrid resampling method, called BOD-based undersampling, which is based on density-based spatial clustering of applications with noise (DBSCAN) approach as well as noise and outlier detection methods, that is, borderline noise factor (BNF) and outlierness based on neighborhood (OBN) to divide majority class samples into four distinctive categories, i.e., safe, borderline noise, rare, and outlier. Specifically, we first determine the borderline noise samples in the overlapped region using the BNF method. Secondly, we use the OBN method to detect outlier samples and apply the DBSCAN approach to cluster the samples. Based on the results obtained from the sample identification analysis, we then segregate the safe category samples which are not abnormal samples while keeping the rest of the samples as rare samples. Finally, we remove some of safe samples by using the random under-sampling (RUS) method and verify the effectiveness of the proposed algorithm through the comprehensive experimental analysis with considering several class imbalance datasets.

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