Singer Gonen, Cohen Izack
Faculty of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel.
Entropy (Basel). 2020 Jul 27;22(8):821. doi: 10.3390/e22080821.
The negative impact of absenteeism on organizations' productivity and profitability is well established. To decrease absenteeism, it is imperative to understand its underlying causes and to identify susceptible employee subgroups. Most research studies apply hypotheses testing and regression models to identify features that are correlated with absenteeism-typically, these models are limited to finding simple correlations. We illustrate the use of interpretable classification algorithms for uncovering subgroups of employees with common characteristics and a similar level of absenteeism. This process may assist human resource managers in understanding the underlying reasons for absenteeism, which, in turn, could stimulate measures to decrease it. Our proposed methodology makes use of an objective-based information gain measure in conjunction with an ordinal CART model. Our results indicate that the ordinal CART model outperforms conventional classifiers and, more importantly, identifies patterns in the data that have not been revealed by other models. We demonstrate the importance of interpretability for human resource management through three examples. The main contributions of this research are (1) the development of an information-based ordinal classifier for a published absenteeism dataset and (2) the illustration of an interpretable approach that could be of considerable value in supporting human resource management decision-making.
旷工对组织生产力和盈利能力的负面影响已得到充分证实。为了减少旷工现象，必须了解其潜在原因并识别易受影响的员工亚组。大多数研究采用假设检验和回归模型来识别与旷工相关的特征——通常，这些模型仅限于发现简单的相关性。我们展示了如何使用可解释的分类算法来揭示具有共同特征和相似旷工水平的员工亚组。这个过程可以帮助人力资源经理理解旷工的潜在原因，进而促使采取措施减少旷工。我们提出的方法结合了基于目标的信息增益度量和有序分类回归树（CART）模型。我们的结果表明，有序分类回归树模型优于传统分类器，更重要的是，它能识别出其他模型未揭示的数据模式。我们通过三个例子展示了可解释性对人力资源管理的重要性。本研究的主要贡献在于：（1）为已发表的旷工数据集开发了一种基于信息的有序分类器；（2）展示了一种可解释的方法，该方法在支持人力资源管理决策方面可能具有相当大的价值。
