Fusing Neural Networks Through Fuzzy Integration

Verikas, Antanas; Lipnickas, Arūnas; Bačauskienė, Marija; Malmqvist, Kerstin

doi:10.1142/9789812778147_0009

Cited by 12 publications

(9 citation statements)

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“…This expresses the correlation between different attributes. Aggregation based on fuzzy integrals-fuzzy integration-possesses this valuable property (Verikas, Lipnickas, Bacauskiene, and Malmqvist 2002). In such schemes, different attributes are fused into a final quality measure by a fuzzy integral with respect to a fuzzy measure.…”

Section: Fuzzy Integration Of Distortion Attributesmentioning

confidence: 99%

Image Analysis and Fuzzy Integration Applied to Print Quality Assessment

Verikas

Bačauskienė

2005

Cybernetics and Systems

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We present an image analysis and fuzzy integration based option for the assessment of print quality in rotogravure printing. Values of several print distortion attributes are evaluated employing image analysis procedures and then are aggregated into an overall print quality measure using fuzzy integration. The experimental investigations performed have shown that the print quality evaluations provided by the measure correlate well with the print quality rankings obtained from the expert. The developed tools are successfully used in printing shops for routine print quality control.

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Section: Fuzzy Integration Of Distortion Attributesmentioning

confidence: 99%

Image Analysis and Fuzzy Integration Applied to Print Quality Assessment

Verikas

Bačauskienė

2005

Cybernetics and Systems

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“…Other proposals include the combination of multiple classifiers [4], [5]. Antanas et al [6] indicated that outputs from multiple neural networks are usually highly correlated, and employ the Choquet fuzzy integral with λ-fuzzy measure is an effective method.…”

Section: Introductionmentioning

confidence: 99%

Combined multiple svm classifiers based on Choquet integral with respect to L- measure

Lin

Huang

2009

2009 International Conference on Machine Learning and Cybernetics

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Combining multiple classifiers is a natural way to explore useful information and improve the performances of individual classifiers. Support vector machine (SVM) has an excellent ability to solve the classification problems. In this study, we try to combine the multiple SVMs which is desirous to gain a more accurate classification than single SVM. When interactions exist in combining multiple SVMs, fuzzy integral with respect to L-measure would be a valid method to fuse these multiple SVMs. From this experiment results, the fusion method based on this fuzzy fusion obtains advancement in terms of the performance of classification.

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“…There are some fusion methods to be used to fusion multiple more simple classifies, for example, majority vote, weighted average, Borda count, Bayes methods, Neural network [4][5] and fuzzy integral [6][7] et.al. Fuzzy integral is often used to fusion multiple classifies when we consider the interaction among different classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…Above all, as fuzzy measures are monotonous set functions, the m2 N coefficients must verify mN2 N-1 constraints so that only constrained optimization techniques can be used [5,7] . This paper use optimization technology to determine fuzzy measures, the experiments demonstrate that the classification accuracy of fuzzy integral with respect to the fuzzy measure is better than the classification accuracies of majority vote and weighted average.…”

Section: Introductionmentioning

confidence: 99%

Determine Fuzzy Measures in Multiple Classifiers Fusion Model

Chen

Guo

Feng

et al. 2006

2006 International Conference on Machine Learning and Cybernetics

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In finite set, Choquet fuzzy integral with respect to fuzzy measures can be transferred into linear combination of product, based on this fact we can choose standard optimization technical to determine fuzzy measures. This paper present linear programming and quadratic programming to determine fuzzy measures, the experiments demonstrate that classification accuracy of fuzzy integral with respect to fuzzy measure is better than the classification accuracies of majority voting and weighted average.

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Fusing Neural Networks Through Fuzzy Integration

Cited by 12 publications

References 0 publications

Image Analysis and Fuzzy Integration Applied to Print Quality Assessment

Image Analysis and Fuzzy Integration Applied to Print Quality Assessment

Combined multiple svm classifiers based on Choquet integral with respect to L- measure

Determine Fuzzy Measures in Multiple Classifiers Fusion Model

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