Learning under concept drift: A review
Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …
data overtime. Concept drift research involves the development of methodologies and …
Smart cities: A survey on data management, security, and enabling technologies
A Gharaibeh, MA Salahuddin… - … Surveys & Tutorials, 2017 - ieeexplore.ieee.org
Integrating the various embedded devices and systems in our environment enables an
Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that …
Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that …
Multimodal trajectory predictions for autonomous driving using deep convolutional networks
Autonomous driving presents one of the largest problems that the robotics and artificial
intelligence communities are facing at the moment, both in terms of difficulty and potential …
intelligence communities are facing at the moment, both in terms of difficulty and potential …
Adaptive random forests for evolving data stream classification
Random forests is currently one of the most used machine learning algorithms in the non-
streaming (batch) setting. This preference is attributable to its high learning performance and …
streaming (batch) setting. This preference is attributable to its high learning performance and …
Machine learning for streaming data: state of the art, challenges, and opportunities
Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …
associated with learning algorithms that update their models given a continuous influx of …
Personalized news recommendation based on click behavior
J Liu, P Dolan, ER Pedersen - … of the 15th international conference on …, 2010 - dl.acm.org
Online news reading has become very popular as the web provides access to news articles
from millions of sources around the world. A key challenge of news websites is to help users …
from millions of sources around the world. A key challenge of news websites is to help users …
On the reliable detection of concept drift from streaming unlabeled data
TS Sethi, M Kantardzic - Expert Systems with Applications, 2017 - Elsevier
Classifiers deployed in the real world operate in a dynamic environment, where the data
distribution can change over time. These changes, referred to as concept drift, can cause the …
distribution can change over time. These changes, referred to as concept drift, can cause the …
Challenges in benchmarking stream learning algorithms with real-world data
VMA Souza, DM dos Reis, AG Maletzke… - Data Mining and …, 2020 - Springer
Streaming data are increasingly present in real-world applications such as sensor
measurements, satellite data feed, stock market, and financial data. The main characteristics …
measurements, satellite data feed, stock market, and financial data. The main characteristics …
Accumulating regional density dissimilarity for concept drift detection in data streams
In a non-stationary environment, newly received data may have different knowledge patterns
from the data used to train learning models. As time passes, a learning model's performance …
from the data used to train learning models. As time passes, a learning model's performance …
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Abstract Concept drift constitutes a challenging problem for the machine learning and data
mining community that frequently appears in real world stream classification problems. It is …
mining community that frequently appears in real world stream classification problems. It is …