Batch-based active learning: Application to social media data for crisis management
D Pohl, A Bouchachia, H Hellwagner - Expert Systems with Applications, 2018 - Elsevier
Classification of evolving data streams is a challenging task, which is suitably tackled with
online learning approaches. Data is processed instantly requiring the learning machinery to …
online learning approaches. Data is processed instantly requiring the learning machinery to …
Analyzing and repairing concept drift adaptation in data stream classification
Data collected over time often exhibit changes in distribution, or concept drift, caused by
changes in factors relevant to the classification task, eg weather conditions. Incorporating all …
changes in factors relevant to the classification task, eg weather conditions. Incorporating all …
A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques
AR MS, CR Nirmala, M Aljohani… - Frontiers in Artificial …, 2022 - frontiersin.org
A financial market is a platform to produce data streams continuously and around 1. 145
Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of …
Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of …
Modeling recurring concepts in data streams: a graph-based framework
Classifying a stream of non-stationary data with recurrent drift is a challenging task and has
been considered as an interesting problem in recent years. All of the existing approaches …
been considered as an interesting problem in recent years. All of the existing approaches …
Learning from data streams: An overview and update
J Read, I Žliobaitė - arXiv preprint arXiv:2212.14720, 2022 - arxiv.org
The literature on machine learning in the context of data streams is vast and growing.
However, many of the defining assumptions regarding data-stream learning tasks are too …
However, many of the defining assumptions regarding data-stream learning tasks are too …
Concept drift estimation with graphical models
L Riso, M Guerzoni - Information Sciences, 2022 - Elsevier
This paper deals with the issue of concept-drift in machine learning in the context of high
dimensional problems. In contrast to previous concept drift detection methods, this …
dimensional problems. In contrast to previous concept drift detection methods, this …
Analyzing concept drift: A case study in the financial sector
AR Masegosa, AM Martínez… - Intelligent Data …, 2020 - content.iospress.com
In this paper, we present a method for exploratory data analysis of streaming data based on
probabilistic graphical models (latent variable models). This method is illustrated by concept …
probabilistic graphical models (latent variable models). This method is illustrated by concept …
Prediction of a complex system with few data: evaluation of the effect of model structure and amount of data with dynamic bayesian network models
A major challenge in environmental modeling is to identify structural changes in the
ecosystem across time, ie, changes in the underlying process that generates the data. In this …
ecosystem across time, ie, changes in the underlying process that generates the data. In this …
Variational inference over nonstationary data streams for exponential family models
In many modern data analysis problems, the available data is not static but, instead, comes
in a streaming fashion. Performing Bayesian inference on a data stream is challenging for …
in a streaming fashion. Performing Bayesian inference on a data stream is challenging for …
AMIDST: A Java toolbox for scalable probabilistic machine learning
The AMIDST Toolbox is an open source Java software for scalable probabilistic machine
learning with a special focus on (massive) streaming data. The toolbox supports a flexible …
learning with a special focus on (massive) streaming data. The toolbox supports a flexible …