Point-wise Diversity Measure and Visualization for Ensemble of Classifiers - With Application to Image Segmentation

Ahmed Al-Taie, Horst K. Hahn, Lars Linsen

2015

Abstract

The idea of using ensembles of classifiers is to increase the performance when compared to applying a single classifier. Crucial to the performance improvement is the diversity of the ensemble. A classifier ensemble is considered to be diverse, if the classifiers make no coinciding errors. Several studies discuss the diversity issue and its relation to the ensemble accuracy. Most of them proposed measures that are based on an ”Oracle” classification. In this paper, we propose a new probability-based diversity measure for ensembles of unsupervised classifiers, i.e., when no Oracle machine exists. Our measure uses a point-wise definition of diversity, which allows for a distinction of diverse and non-diverse areas. Moreover, we introduce the concept of further categorizing the diverse areas into healthy and unhealthy diversity areas. A diversity area is healthy for the ensemble performance, if there is enough redundancy to compensate for the errors. Then, the performance of the ensemble can be based on two parameters, the non-diversity area, i.e., the size of all regions where the classifiers of the ensemble agree, and the healthy diversity area, i.e., the size of the regions where the diversity is healthy. Furthermore, our point-wise diversity measure allows for an intuitive visualization of the ensemble diversity for visual ensemble performance comparison in the context of image segmentation.

Paper Citation

in Harvard Style

Al-Taie A., Hahn H. and Linsen L. (2015). Point-wise Diversity Measure and Visualization for Ensemble of Classifiers - With Application to Image Segmentation . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 569-576. DOI: 10.5220/0005309605690576

in Bibtex Style

@conference{visapp15,
author={Ahmed Al-Taie and Horst K. Hahn and Lars Linsen},
title={Point-wise Diversity Measure and Visualization for Ensemble of Classifiers - With Application to Image Segmentation},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={569-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005309605690576},
isbn={978-989-758-089-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Point-wise Diversity Measure and Visualization for Ensemble of Classifiers - With Application to Image Segmentation
SN - 978-989-758-089-5
AU - Al-Taie A.
AU - Hahn H.
AU - Linsen L.
PY - 2015
SP - 569
EP - 576
DO - 10.5220/0005309605690576