"The dearest of our possessions": applying Floridi's information privacy concept in models of information behavior and information literacy

This conceptual paper argues for the value of an approach to privacy in the digital information environment informed by Luciano Floridi's philosophy of information and information ethics. This approach involves achieving informational privacy, through the features of anonymity and obscurity, through an optimal balance of ontological frictions. This approach may be used to modify models for information behavior and for information literacy, giving them a fuller and more effective coverage of privacy issues in the infosphere. For information behavior, the Information Seeking and Communication Model, and the Information Grounds conception, are most appropriate for this purpose. For information literacy, the metaliteracy model, using a modification a privacy literacy framework, is most suitable

Joint European Conference on Machine Learning and Knowledge Discovery in Databases

This paper proposes one-class quantification, a new Machine Learning task. Quantification estimates the class distribution of an unlabeled sample of instances. Similarly to one-class classification, we assume that only a sample of examples of a single class is available for learning, and we are interested in counting the cases of such class in a test set. We formulate, for the first time, one-class quantification methods and assess them in a comprehensible open-set evaluation. In an open-set problem, several “subclasses” represent the negative class, and we cannot assume to have enough observations for all of them at training time. Therefore, new classes may appear after deployment, making this a challenging setup for existing quantification methods. We show that our proposals are simple and more accurate than the state-of-the-art in quantification. Finally, the approaches are very efficient, fitting batch and stream applications. Code related to this paper is available at: https://github.com/denismr/One-class-Quantification

Combining instance selection and self-training to improve data stream quantification

Abstract In the last years, learning from data streams has attracted the attention of researchers and practitioners due to its large number of applications. These applications have motivated the research community to propose a significant amount of methods to solve problems in diverse tasks, more prominently in classification, clustering, and anomaly detection. However, a relevant task known as quantification has remained mostly unexplored. The quantification goal is to provide an estimate of the class prevalence in an unlabeled set. Recently, we proposed the SQSI algorithm to quantify data streams with concept drifts. SQSI uses a statistical test to identify concept drifts and retrain the classifiers. However, the retraining involves requiring the labels for all newly arrived instances. In this paper, we extend SQSI algorithm by exploring instance selection techniques allied to semi-supervised learning. The idea is to request the classes of a smaller subset of recent examples. Our experiments demonstrate that SQSI’s extension significantly reduces the dependency on actual labels while maintaining or improving the quantification accuracy

Die Literaturrundschau

Die Literaturrundschau dieser Ausgabe von Communicatio Socialis