In many modern data analysis scenarios the first and most urgent task consists of reducing the redundancy in high dimensional input spaces. A method is presented that quantifies the discriminative power of the input features in a fuzzy model. A possibilistic information measure of the model is defined on the basis of the available fuzzy rules and the resulting possibilistic information gain, as...

Construction of Fuzzy Control Charts Based on Weighted Possibilistic Mean Dabuxilatu Wang, Pinghui Li & Masami Yasuda a School of Economics and Statistics, Guangzhou University, Guangzhou, China b Research Center of Statistical Science Lingnan, Guangzhou University, Guangzhou, China c Faculty of Science, Chiba University, Chiba, Japan Accepted author version posted online: 16 Apr 2014.Published...

In this paper, I provide the basis for a measureand integral-theoretic formulation of possibility theory. It is shown that, using a general definition of possibility measures, and a generalization of Sugeno’s fuzzy integral – the seminormed fuzzy integral, or possibility integral –, a unified and consistent account can be given of many of the possibilistic results extant in the literature. The ...

It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...

This paper continues the authors’ research in stability analysis in possibilistic programming in that it extends the results in [7] to possibilistic linear programs with multiple objective functions. Namely, we show that multiobjective possibilistic linear programs with continuous fuzzy number coefficients are well-posed, i.e. small changes in the membership function of the coefficients may cau...

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzz...