loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Cristhian Molina 1 ; Vincent Bombardier 2 and Patrick Charpentier 2

Affiliations: 1 CNRS, CRAN, UMR 7039 and Universidad de Santiago de Chile, France ; 2 Université de Lorraine, CNRS, CRAN and UMR 7039, France

ISBN: 978-989-758-157-1

Keyword(s): Feature Selection, Fuzzy Associative Rules, Pattern Recognition, Fuzzy Rule Classifier.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Fuzzy Image, Speech and Signal Processing, Vision and Multimedia ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Pattern Recognition: Fuzzy Clustering and Classifiers ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In this paper, two ways for automatically designing a hierarchical classifier is checked. This study deals with a specific context where is necessary to work with a few number of training samples (and often unbalanced), to manage the subjectivity of the different output classes and to take into account an imprecision degree in the input data. The aim is also to create an interpretable classification system by reducing its dimensionality with the use of Feature Selection and Fuzzy Association Rules generation. The obtained results over an industrial wood datasets prove their efficacy to select input feature and they are used to make some conclusions about their performance. Finally, an original methodology to automatically build a hierarchical classifier is proposed by merging the both previous methods. Each node of the hierarchical structure corresponds to a Fuzzy Rules Classifier with selected inputs and macro classes for output. The leaves are the outputs of the classification syste m. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.233.224.8

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Molina, C.; Bombardier, V. and Charpentier, P. (2015). Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2 FCTA: FCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 150-155. DOI: 10.5220/0005595401500155

@conference{fcta15,
author={Cristhian Molina. and Vincent Bombardier. and Patrick Charpentier.},
title={Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2 FCTA: FCTA, (ECTA 2015)},
year={2015},
pages={150-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005595401500155},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2 FCTA: FCTA, (ECTA 2015)
TI - Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier
SN - 978-989-758-157-1
AU - Molina, C.
AU - Bombardier, V.
AU - Charpentier, P.
PY - 2015
SP - 150
EP - 155
DO - 10.5220/0005595401500155

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.