Dynamic Selection of Classifiers for Fusing Imbalanced Heterogeneous Data

Sergey Sukhanov; Christian Debes; Abdelhak M. Zoubir

ICASSP 2019accepted0 citations

Dynamic Selection of Classifiers for Fusing Imbalanced Heterogeneous Data

Sergey Sukhanov, Christian Debes, Abdelhak M. Zoubir

Abstract

Data fusion (DF) from multiple heterogeneous sources is a typical task for many multisensor applications including remote sensing classification problems. Multiple classifier systems (MCS) provide a natural way to solve DF on the decision level by training individual classifiers separately on its own data source and then combine their outputs. In this paper, we consider a dynamic selection (DS) framework to select and fuse competent classifiers of MCS. For this, we propose a competence estimation and selection method to improve the performance of the DF system especially under class imbalance. We evaluate the method with synthetic and real datasets, demonstrating the applicability of the proposed framework.

BibTeX

@inproceedings{icassp2019_dynamicselection,
  title = {Dynamic Selection of Classifiers for Fusing Imbalanced Heterogeneous Data},
  author = {Sergey Sukhanov and Christian Debes and Abdelhak M. Zoubir},
  booktitle = {ICASSP 2019},
  year = {2019}
}