A Survey on Concept Drift Adaptation [PDF]
ACM Computing Surveys, 2014 Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time.
Bifet, A. +4 more
core +9 more sources
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift [PDF]
Frontiers in Artificial IntelligenceIn an increasing number of industrial and technical processes, machine learning-based systems are being entrusted with supervision tasks. While they have been successfully utilized in many application areas, they frequently are not able to generalize to ...
Fabian Hinder +2 more
doaj +3 more sources
A survey on detecting healthcare concept drift in AI/ML models from a finance perspective [PDF]
Frontiers in Artificial Intelligence, 2023 Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion.
Abdul Razak M. S. +4 more
doaj +3 more sources
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift [PDF]
Frontiers in Artificial IntelligenceThe world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes.
Fabian Hinder +2 more
doaj +3 more sources
Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
Applied Sciences, 2023 With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become ...
Qiuyan Xiang +3 more
doaj +2 more sources
Model Based Explanations of Concept Drift [PDF]
Neurocomputing, 2023 The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become inaccurate and need adjustment.
Fabian Hinder +3 more
semanticscholar +4 more sources
Boosting Classifiers for Drifting Concepts [PDF]
Intelligent Data Analysis, 2007 This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners.
Klinkenberg, Ralf, Scholz, Martin
core +6 more sources
Streaming Data Classification Based on Hierarchical Concept Drift and Online Ensemble
IEEE Access, 2023 In order to improve the performance of online learning in the real-time distribution of streaming data, a streaming data classification algorithm based on hierarchical concept drift and online ensemble(SCHCDOE) is proposed in this paper.
Ning Liu, Jianhua Zhao
doaj +2 more sources
Adaptive Online Sequential ELM for Concept Drift Tackling. [PDF]
Comput Intell Neurosci, 2016 A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM ...
Budiman A, Fanany MI, Basaruddin C.
europepmc +6 more sources
Journal of Cloud Computing: Advances, Systems and ApplicationsThe detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates ...
Jing Chen +5 more
doaj +2 more sources