Looking the organization's "backstages": political leadership in health organizations

Although leadership investigation has become for the last years an election topic with major relevance on organizational studies and accepting peacefully the general idea that organizations are freeland for politics, all these acceptances run against a kind of “fear” from the academy scholars on approaching the political leaderships’ singularities on organizations. Indeed, when we cross over both phenomena we verify that the absence and weaknesses towards the unique characteristics of political leadership on work scenarios are becoming sharped regarding to their predictors, their workers and their organizations, even if we left aside its moderator variables.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.