• Corpus ID: 233715019

Automatic Learning to Detect Concept Drift

@article{Yu2021AutomaticLT,
  title={Automatic Learning to Detect Concept Drift},
  author={Hang Yu and Tianyu Liu and Jie Lu and Guangquan Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.01419}
}
Many methods have been proposed to detect concept drift, i.e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms. However, the most of current detection methods are based on the assessment of the degree of change in the data distribution, cannot identify the type of concept drift. In this paper, we propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework that learns to classify concept drift… 
1 Citations

Figures and Tables from this paper

PENGEMBANGAN SENSOR-CLOUD PADA SMART CITY UNTUK MENGHADIRKAN KETERSEDIAAN DATA WAKTU NYATA

Pengembangan dan operasional dari sebuah Smart-City bergantung sepenuhnya pada lebih dari satu kelompok sumber data waktu nyata. Bentuk integrasi data ini kemungkinan dapat terwujud melalui manajemen

References

SHOWING 1-10 OF 30 REFERENCES

Learning under Concept Drift: A Review

TLDR
A high quality, instructive review of current research developments and trends in the concept drift field is conducted, and a framework of learning under concept drift is established including three main components: concept drift detection, concept drift understanding, and concept drift adaptation.

Regional Concept Drift Detection and Density Synchronized Drift Adaptation

TLDR
This work proposes a local drift degree (LDD) measurement that can continuously monitor regional density changes and synchronizes the regional density discrepancies according to LDD to retrieve nondrifted information from suspended historical data.

A survey on concept drift adaptation

TLDR
The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.

Early Drift Detection Method

TLDR
A method for detecting concept drift, even in the case of slow gradual change, based on the estimated distribution of the distances between classiflcation errors that can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental and online algorithm.

Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift

TLDR
Experiments on both synthetic and real data sets with concept drift show that DWMIL performs better than the state-of-the-art competitors, with less computational cost.

Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds

TLDR
Two main approaches to handle concept drift regardless of the learning model are proposed, the first one involves moving averages and is more suitable to detect abrupt changes and the second follows a widespread intuitive idea to deal with gradual changes using weighted moving averages.

Concept drift detection based on Fisher's Exact test

Robust Prototype-Based Learning on Data Streams

TLDR
It is demonstrated that the new data stream classification approach, SyncStream, is capable of dynamically modeling the evolving concepts from even a small set of prototypes and tolerant of inappropriate or noisy examples via error-driven representativeness learning.

Adaptive Ensemble Active Learning for Drifting Data Stream Mining

TLDR
This paper proposes a novel active learning approach based on ensemble algorithms that is capable of using multiple base classifiers during the label query process, and is a plug-in solution, capable of working with most of existing streaming ensemble classifiers.