Investigating the Benefits of Exploiting Incremental Learners Under Active Learning Scheme

Part 2: AI Anomaly Detection - Active LearningInternational audienceThis paper examines the efficacy of incrementally updateable learners under the Active Learning concept, a well-known iterative semi-supervised scheme where the initially collected instances, usually a few, are augmented by the combined actions of both the chosen base learner and the human factor. Instead of exploiting conventional batch-mode learners and refining them at the end of each iteration, we introduce the use of incremental ones, so as to apply favorable query strategies and detect the most informative instances before they are provided to the human factor for annotating them. Our assumption about the benefits of this kind of combination into a suitable framework is verified by the achieved classification accuracy against the baseline strategy of Random Sampling and the corresponding learning behavior of the batch-mode approaches over numerous benchmark datasets, under the pool-based scenario. The measured time reveals also a faster response of the proposed framework, since each constructed classification model into the core of Active Learning concept is built partially, updating the existing information without ignoring the already processed data. Finally, all the conducted comparisons are presented along with the appropriate statistical testing processes, so as to verify our claim

Big data in mHealth

The proliferation of mobile technologies has paved the way for the widespread use of mobile health (mHealth) devices. This in turn generates a large amount of data, which is essentially big data, that can be used for various purposes. In order to obtain the maximum benefit from mHealth data, emerging big data technologies can be employed. In this chapter, the relationship between mHealth and big data is investigated from a sociotechnical perspective. Following an overview of the state-of-the-art, stakeholders and their interests are identified, and the impact of big data on such interests is presented. The opportunities of using big data technologies in the mHealth domain are considered from several viewpoints. Social and economic implications of using big data technologies toward these ends are highlighted. Various challenges exist in the implementation and adoption of mHealth data processing. While there are social challenges including privacy, safety, and a false sense of confidence, there are also technical challenges such as security, standardization, correctness, timely analysis, and domain expertise. Some of these coincide with the challenges of the big data domain, and the others are related to human nature and human capabilities. The use of existing big data platforms requires significant expertise and know-how in data science domain which may hinder the adoption of big data technologies in mHealth. Hence, a solution in the form of a framework that provides higher abstraction level programming models is suggested to facilitate widespread user adoption. Accordingly, user aspects associated with big data in the mHealth domain are discussed