Multi-Scale Fuzzy Feature Selection Method applied to Wood Singularity Identification

DOI

10.2991/ijcis.2018.25905185How to use a DOI?

Keywords

Image processing; Fuzzy logic; Pattern recognition; Feature selection; Choquet integral

Abstract

A multi-scale feature selection method based on the Choquet Integral is presented in this paper. Usually, aggregation decision-making problems are well solved, relying on few decision rules associated to a small number of input parameters. However, many industrial applications require the use of numerous features although not all of them will be relevant. Thus, a new feature selection model is proposed to achieve a suitable set of input features while reducing the complexity of the decision-making problem. First, a new criterion, combining the importance of the parameters as well as their interaction indices is defined to sort them out by increasing impact. Then, this criterion is embedded into a new random parameter space partitioning algorithm. Last, this new feature selection method is applied to an industrial wood singularity identification problem. The experimental study is based on the comparative analysis of the results obtained from the process of selecting parameters in several feature selection methods. The experimental study attests to the relevance of the remaining set of selected parameters.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Vincent BOMBARDIER
AU  - Laurent WENDLING
PY  - 2018
DA  - 2018/11/01
TI  - Multi-Scale Fuzzy Feature Selection Method applied to Wood Singularity Identification
JO  - International Journal of Computational Intelligence Systems
SP  - 108
EP  - 122
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.25905185
DO  - 10.2991/ijcis.2018.25905185
ID  - BOMBARDIER2018
ER  -