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
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 syst
em.
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