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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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Adaptive Robust Local Online Density Estimation for Streaming Data.

Zhong Chen1, Zhide Fang2, Victor Sheng3

  • 1Department of Computer Science, Xavier University of Louisiana, New Orleans LA, USA.

International Journal of Machine Learning and Cybernetics
|June 21, 2021
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Summary
This summary is machine-generated.

This study introduces adaptive local online kernel density estimators (ALoKDE) for improved real-time density estimation. These methods enhance adaptability and robustness for concept-drifting and noisy data streams.

Keywords:
Adaptive bandwidth selectionAdaptive weighting factor optimizationEnsemble learningLocal samplingOnline density estimationStreaming data

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Area of Science:

  • Machine Learning
  • Data Mining
  • Statistical Modeling

Background:

  • Online density estimation is vital for streaming data applications.
  • Existing methods struggle with concept drift and noisy data, leading to poor approximations.

Purpose of the Study:

  • To propose an adaptive local online kernel density estimator (ALoKDE) for real-time density estimation.
  • To develop a robust variant (R-ALoKDE) for handling noisy data streams.
  • To analyze the theoretical properties and error bounds of the proposed estimators.

Main Methods:

  • Developed ALoKDE with integrated concept drift detection and adaptive weighted estimation.
  • Introduced R-ALoKDE for enhanced noise handling.
  • Analyzed asymptotic properties and derived theoretical error bounds (bias, variance, MSE, MISE).

Main Results:

  • ALoKDE and R-ALoKDE demonstrate improved adaptability and robustness on concept-drifting and noisy data.
  • Comparative studies confirm the efficacy of ALoKDE and R-ALoKDE in online density estimation and classification tasks.
  • Theoretical analysis provides error bounds for bias, variance, MSE, and MISE.

Conclusions:

  • ALoKDE and R-ALoKDE offer significant improvements for online density estimation in dynamic environments.
  • The proposed methods are effective for real-time classification with noisy streaming data.
  • Theoretical guarantees support the practical performance of the estimators.