A Fuzzy Reasoning Classification Method for Pattern Recognition

2007 IEEE International Fuzzy Systems Conference

Charpentier

2007

-This article presents a self-fuzzification method to enhance the settings of a Fuzzy Reasoning Classification adapted to the automated inspection of wooden boards. The supervised classification is made thanks to fuzzy linguistic rules generated from small training data sets. This study especially answers to a double industrial need about the pattern recognition in wooden boards. Firstly, few samples are available to generate the recognition model. This aspect makes lesser efficient compilation methods like neural networks in terms of recognition rates. Secondly, the settings of the classification method must be simplified, because the users are not experts in fuzzy logic. In this article, two points are presented. The first part demonstrates the generalization capability of the presented classification method in comparison to more classical algorithms. In the second part, we propose a new automatic method of parameter fuzzification, by using the typicality correlation coefficients of each class.

“…According to the scale defined by Landis and Koch [14] our model is considered as excellent. Nevertheless, these results strongly depend on the fuzzification step [13].…”

Section: Generalization Capabilities Of the Frcmentioning

confidence: 97%

“…It allows generating "IF… THEN…" fuzzy rules to define the different output classes. We distinguish three parts: the parameter fuzzification, the rules generation and the model adjustment [13]. Then the obtained rules are used to identify the different "non-labelled" samples.…”

mentioning

confidence: 99%

Self-Fuzzification Method according to Typicality Correlation for Classification on tiny Data Sets

Schmitt

2007 IEEE International Fuzzy Systems Conference

Charpentier

2007

“…The fuzzy reasoning classifier [24] is based on a fuzzy linguistic rule mechanism. It is well adapted to the presented industrial application.…”

Section: Fuzzy Linguistic Rule Classifiermentioning

confidence: 99%

Improving Fuzzy Rule Classifier by Extracting Suitable Features From Capacities With Respect to the Choquet Integral

Schmitt

IEEE Trans. Syst., Man, Cybern. B

Wendling

2008

Self Cite

Abstract-In this paper, an iterative method to select suitable features in an industrial pattern recognition context is proposed. It combines a global method of feature selection and a fuzzy linguistic rule classifier. It is applied to an industrial fabric textile context. The aim of the global vision system is to identify textile fabric defects. From the related industrial process, the training data sets are small, and some are incomplete. Moreover, the recognition step must be compatible with the time constant of the system, which generally imposes low complexity for the system. The choice of the most relevant features and the reduction of their number are important to respect these constraints. The feature selection method is based on the analysis of indexes extracted on the lattice defined from training in relation with the Choquet integral. This selection step is embedded in an iterative algorithm to discard weaker features in order to decrease the number of rules while keeping good recognition rates. The recognition step is done with a fuzzy reasoning classifier that is well adapted for this application case. The proposed method is quite efficient with small learning data sets because of the generalization capacity of both the feature selection and recognition steps. The experimental study shows the wanted behavior of this approach: the feature number decreases, whereas the recognition rate increases. Thus, the total number of generated fuzzy rules is reduced.

“…The figure 4 represents the rough principle of the Fuzzy Reasoning Classifier (FRC) developed for our application, underlying the training and the generalization steps. For more details about the inference mechanism implemented, the reader can refer to [25].…”

Section: Fuzzy Inference Enginementioning

confidence: 99%

A feature selection method based on Choquet Integral and Typicality Analysis

Mazaud

Rendek

2007 IEEE International Fuzzy Systems Conference

et al. 2007

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