949 research outputs found
Next challenges for adaptive learning systems
Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p
Data science applications to connected vehicles: Key barriers to overcome
The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor
Tracking recurrent concepts using context
The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Unlabelled data appear in many domains and are particularly relevant to
streaming applications, where even though data is abundant, labelled data is
rare. To address the learning problems associated with such data, one can
ignore the unlabelled data and focus only on the labelled data (supervised
learning); use the labelled data and attempt to leverage the unlabelled data
(semi-supervised learning); or assume some labels will be available on request
(active learning). The first approach is the simplest, yet the amount of
labelled data available will limit the predictive performance. The second
relies on finding and exploiting the underlying characteristics of the data
distribution. The third depends on an external agent to provide the required
labels in a timely fashion. This survey pays special attention to methods that
leverage unlabelled data in a semi-supervised setting. We also discuss the
delayed labelling issue, which impacts both fully supervised and
semi-supervised methods. We propose a unified problem setting, discuss the
learning guarantees and existing methods, explain the differences between
related problem settings. Finally, we review the current benchmarking practices
and propose adaptations to enhance them
On-device modeling of user's social context and familiar places from smartphone-embedded sensor data
Context modeling and recognition are crucial for adaptive mobile and
ubiquitous computing. Context-awareness in mobile environments relies on prompt
reactions to context changes. However, current solutions focus on limited
context information processed on centralized architectures, risking privacy
leakage and lacking personalization. On-device context modeling and recognition
are emerging research trends, addressing these concerns. Social interactions
and visited locations play significant roles in characterizing daily life
scenarios. This paper proposes an unsupervised and lightweight approach to
model the user's social context and locations directly on the mobile device.
Leveraging the ego-network model, the system extracts high-level, semantic-rich
context features from smartphone-embedded sensor data. For the social context,
the approach utilizes data on physical and cyber social interactions among
users and their devices. Regarding location, it prioritizes modeling the
familiarity degree of specific locations over raw location data, such as GPS
coordinates and proximity devices. The effectiveness of the proposed approach
is demonstrated through three sets of experiments, employing five real-world
datasets. These experiments evaluate the structure of social and location ego
networks, provide a semantic evaluation of the proposed models, and assess
mobile computing performance. Finally, the relevance of the extracted features
is showcased by the improved performance of three machine learning models in
recognizing daily-life situations. Compared to using only features related to
physical context, the proposed approach achieves a 3% improvement in AUROC, 9%
in Precision, and 5% in Recall
- …