Emotions in organizations: A multi-level perspective

Recent years have seen an upsurge of interest in the study of emotions in organizations. Research, however, has been hampered by the ephemeral nature of emotions and a lack of an integrated multi-level model. This article therefore presents a five-level model of emotions in organizations. At the lowest level is within-person variation, defined in terms of affective events theory. Levels of the model then proceed through individual, dyadic relationship, group, and organization-wide perspectives. The article also outlines the neurophysiological processes that underlie the experience, perception, and communication of emotion; it concludes with a discussion of implications for research and practice.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.