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 …
No free lunch theorem for concept drift detection in streaming data classification: A review
H Hu, M Kantardzic, TS Sethi - Wiley Interdisciplinary Reviews …, 2020 - Wiley Online Library
Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …
are unlabeled because the sheer volume of the stream makes it impractical to label a …
[HTML][HTML] Unsupervised real-time anomaly detection for streaming data
We are seeing an enormous increase in the availability of streaming, time-series data.
Largely driven by the rise of connected real-time data sources, this data presents technical …
Largely driven by the rise of connected real-time data sources, this data presents technical …
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey
Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …
availability of a historical dataset for model development, and that the resulting model will, to …
A one-class classification approach for bot detection on Twitter
J Rodríguez-Ruiz, JI Mata-Sánchez, R Monroy… - Computers & …, 2020 - Elsevier
Twitter is a popular online social network with hundreds of millions of users, where n
important part of the accounts in this social network are not humans. Approximately 48 …
important part of the accounts in this social network are not humans. Approximately 48 …
Evolving ensemble fuzzy classifier
The concept of ensemble learning offers a promising avenue in learning from data streams
under complex environments because it better addresses the bias and variance dilemma …
under complex environments because it better addresses the bias and variance dilemma …
Nonstationary data stream classification with online active learning and siamese neural networks✩
We have witnessed in recent years an ever-growing volume of information becoming
available in a streaming manner in various application areas. As a result, there is an …
available in a streaming manner in various application areas. As a result, there is an …
An evolving approach to data streams clustering based on typicality and eccentricity data analytics
In this paper we propose an algorithm for online clustering of data streams. This algorithm is
called AutoCloud and is based on the recently introduced concept of Typicality and …
called AutoCloud and is based on the recently introduced concept of Typicality and …
Evolving fuzzy and neuro-fuzzy systems: Fundamentals, stability, explainability, useability, and applications
E Lughofer - Handbook on Computer Learning and Intelligence …, 2022 - World Scientific
This chapter provides an all-round picture of the development and advances in the fields of
evolving fuzzy systems (EFS) and evolving neuro-fuzzy systems (ENFS) which have been …
evolving fuzzy systems (EFS) and evolving neuro-fuzzy systems (ENFS) which have been …
Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams
The concept of SCN offers a fast framework with universal approximation guarantee for
lifelong learning of non-stationary data streams. Its adaptive scope selection property …
lifelong learning of non-stationary data streams. Its adaptive scope selection property …