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Open AccessArticle

Augmenting Deep Learning Performance in an Evidential Multiple Classifier System

1
SATIE - CNRS UMR 8029, Paris-Sud University, Paris-Saclay University, 91405 Orsay CEDEX, France
2
SAFRAN SA, Safran Tech, Pole Technologie du Signal et de l’Information, 78772 Magny-les-Hameaux, France
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(21), 4664; https://doi.org/10.3390/s19214664
Received: 30 September 2019 / Revised: 23 October 2019 / Accepted: 24 October 2019 / Published: 27 October 2019
(This article belongs to the Special Issue Deep Learning for Multi-Sensor Fusion)
The main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier’s respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision. View Full-Text
Keywords: deep learning; ensemble classifiers; Belief Function Theory; pedestrian detection; high-density crowds deep learning; ensemble classifiers; Belief Function Theory; pedestrian detection; high-density crowds
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Vandoni, J.; Hégarat-Mascle, S.L.; Aldea, E. Augmenting Deep Learning Performance in an Evidential Multiple Classifier System. Sensors 2019, 19, 4664.

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