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Experimenting multiresolution analysis for identifying regions of different classification complexity

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Abstract

Systems for assessing the classification complexity of a dataset have received increasing attention in research activities on pattern recognition. These systems typically aim at quantifying the overall complexity of a domain, with the goal of comparing different datasets. In this work, we propose a method for partitioning a dataset into regions of different classification complexity, so to highlight sources of complexity inside the dataset. Experiments have been carried out on relevant datasets, proving the effectiveness of the proposed method.

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Notes

  1. Of course, a similar definition can also be given for the dataset \(\mathbf {D}\).

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Acknowledgments

Emanuele Tamponi gratefully acknowledges Sardinia Regional Government for the financial support of his PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2007–2013 - Axis IV Human Resources, Objective l.3, Line of Activity l.3.1.).

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

    G. Armano & E. Tamponi

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  1. G. Armano
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  2. E. Tamponi
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Correspondence to E. Tamponi.

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Armano, G., Tamponi, E. Experimenting multiresolution analysis for identifying regions of different classification complexity. Pattern Anal Applic 19, 129–137 (2016). https://doi.org/10.1007/s10044-014-0446-y

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