Neuro-genetic approach to multidimensional fuzzy reasoning for pattern classification

Ray, Kumar Sankar; Ghoshal, Jayati

doi:10.1016/s0165-0114(98)00013-x

Cited by 15 publications

(17 citation statements)

References 9 publications

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“…If we defuzzy a fuzzy vector we obtain a crisp vector. 6 Detail treatments on fuzzy vector will be reported elsewhere. Here, in the paper we simply introduce the basic notion of fuzzy vector which is useful for the present Design Study.…”

Section: Remark 12mentioning

confidence: 99%

“…Here, in the paper we simply introduce the basic notion of fuzzy vector which is useful for the present Design Study. 5,6 …”

Section: Remark 12mentioning

confidence: 99%

“…Instead, from a given observation (rule) we can represent/locate a collection of population of patterns in an area (in case of R 2 ; but a region in general) on the pattern space by a FPV which is a very natural representation of the antecedent part of a MFI. 5,6 Multidimensional fuzzy implication A, y is B) then z is C" where A, B, C are fuzzy sets is not merely a collection of one-dimensional implications, a conventional interpretation is usually taken in the multi-dimensional case. For example, according to the conventional interpretation, the above two-dimensional implication is translated…”

Section: Appendix Amentioning

confidence: 99%

See 2 more Smart Citations

Pattern Recognition Based on Fuzzy Set and Genetic Algorithm

Ray

2014

Int. J. Image Grap.

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In this paper, we consider a soft computing approach to pattern classification. Our basic tools for soft computing are fuzzy relational calculus (FRC) and genetic algorithm (GA). We introduce a new interpretation of multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. We also consider the notion of a fuzzy pattern vector to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation R i between the antecedent clause and consequent clause of each one-dimensional fuzzy implication. For the estimation of R i we use floating point representation of GA. Thus a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non-fuzzy features of a test pattern can be classified. The performance of the proposed scheme is tested on synthetic data. Subsequently, we use the proposed scheme for the vowel classification problem of an Indian language. Finally, a benchmark of performance is established by considering multiplayer perception (MLP), support vector machine (SVM) and the present method. The Abalone, Hosse Colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. This new tool for pattern classification is very effective for classification of patterns under vegue and imprecise environment. It can provide multiple classification under overlapped classes.

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Section: Remark 12mentioning

confidence: 99%

“…Here, in the paper we simply introduce the basic notion of fuzzy vector which is useful for the present Design Study. 5,6 …”

Section: Remark 12mentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

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Pattern Recognition Based on Fuzzy Set and Genetic Algorithm

Ray

2014

Int. J. Image Grap.

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“…To establish the classification model, artificial neural networks have also been applied successfully to the M-class pattern classification problems (Brameier & Banzhaf, 2001; Ray & Ghoshal, 2000). Neural network techniques lie in high computation rates and information encoding among the various connection weights.…”

Section: Introductionmentioning

confidence: 99%

Design of a two-stage fuzzy classification model

Guo

Cheng

2008

Expert Systems with Applications

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“…In the fields of theoretical study, applying the expert system to the classification problem and pattern recognition has drawn the considerable attention of many researchers for the last decade (Ishibuchi, Nozaki, Yamamoto, & Tanaka, 1995;Li, Guo, & Kuo, 2005;Ravi & Zimmermann, 2000;Ray & Ghoshal, 2000;Ye & Li, 2002;Yuan & Zhuang, 1996). However, an expert system often includes a great quantity of the knowledge base and spends a lot of time to establish it.…”

Section: Introductionmentioning

confidence: 99%