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Diversity-aware classifier ensemble selection via f-score

Visentini, Ingrid
•
SNIDARO, Lauro
•
FORESTI, Gian Luca
2016
  • journal article

Periodico
INFORMATION FUSION
Abstract
he primary effect of using a reduced number of classifiers is a reduction in the computational requirements during learning and classification time. In addition to this obvious result, research shows that the fusion of all available classifiers is not a guarantee of best performance but good results on the average. The much researched issue of whether it is more convenient to fuse or to select has become even more of interest in recent years with the development of the Online Boosting theory, where a limited set of classifiers is continuously updated as new inputs are observed and classifications performed. The concept of online classification has recently received significant interest in the computer vision community. Classifiers can be trained on the visual features of a target, casting the tracking problem into a binary classification one: distinguishing the target from the background. Here we discuss how to optimize the performance of a classifier ensemble employed for target tracking in video sequences. In particular, we propose the F-score measure as a novel means to select the members of the ensemble in a dynamic fashion. For each frame, the ensemble is built as a subset of a larger pool of classifiers selecting its members according to their F-score. We observed an overall increase in classification accuracy and a general tendency in redundancy reduction among the members of an f-score optimized ensemble. We carried out our experiments both on benchmark binary datasets and standard video sequences. © 2015 Elsevier B.V. All rights reserved.
DOI
10.1016/j.inffus.2015.07.003
WOS
WOS:000364247900003
Archivio
http://hdl.handle.net/11390/1086763
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84939175565
http://www.sciencedirect.com/science/article/pii/S1566253515000688
Diritti
closed access
Soggetti
  • Classifiers fusion

  • Classifiers selection...

  • F-score

  • Online tracking

  • Tracking via classifi...

  • Signal Processing

  • Software

  • Hardware and Architec...

  • Information System

  • NEURAL-NETWORK ENSEMB...

  • COMBINING CLASSIFIERS...

  • TRACKING

  • PRECISION

  • FUSION

Scopus© citazioni
31
Data di acquisizione
Jun 7, 2022
Vedi dettagli
Web of Science© citazioni
34
Data di acquisizione
Mar 26, 2024
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