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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Augmenting Deep Learning Performance in an Evidential Multiple Classifier System.

Jennifer Vandoni1,2, Sylvie Le Hégarat-Mascle1, Emanuel Aldea1

  • 1SATIE - CNRS UMR 8029, Paris-Sud University, Paris-Saclay University, 91405 Orsay CEDEX, France.

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PubMed
Summary

Ensemble methods combining deep learning and support vector machines improve pedestrian detection in crowded scenes. This approach effectively models uncertainty, enhancing performance and interpretability for limited labeled data scenarios.

Keywords:
Belief Function Theorydeep learningensemble classifiershigh-density crowdspedestrian detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Deep learning models often require large labeled datasets, which are costly and time-consuming to acquire.
  • Traditional methods struggle with complex scenes like high-density crowds due to annotation imprecision and data scarcity.
  • Ensemble methods offer a way to improve model robustness and performance by combining multiple classifiers.

Purpose of the Study:

  • To investigate the effectiveness of ensemble methods for deep learning with limited labeled data.
  • To develop a robust system for pedestrian detection in challenging, high-density crowd scenarios.
  • To leverage the strengths of different classifiers within an evidential framework for uncertainty modeling.

Main Methods:

  • Utilizing an ensemble of neural networks with Monte Carlo dropout for deep learning feature extraction.
  • Employing an ensemble of Support Vector Machine (SVM) classifiers with hand-crafted features and active learning.
  • Combining diverse classifiers within an evidential framework to model imprecision and uncertainty.

Main Results:

  • The proposed Multiple Classifier System (MCS) demonstrated improved performance in pedestrian detection compared to individual classifiers.
  • Effective modeling of uncertainty through evidential fusion led to enhanced detection accuracy.
  • The system provided deeper insights into decision-making through the commitment of evidence.

Conclusions:

  • Ensemble methods, particularly when combined with evidential frameworks, are highly applicable to deep learning tasks with limited labeled data.
  • The developed system offers a promising solution for challenging computer vision problems like pedestrian detection in dense crowds.
  • Modeling uncertainty is crucial for improving both performance and interpretability in machine learning systems.