Intuitionistic L-fuzzy Rough Sets, Intuitionistic L-fuzzy Preorders and Intuitionistic L-fuzzy TopologiesZhong, Yu; Yan, Cong-Hua
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2016.11.002
AbstractIn this paper, by constructing an intuitionistic -fuzzy triangle norm and an intuitionistic -fuzzy implicator , lower and upper approximations of intuitionistic -fuzzy sets are defined with respect to an intuitionistic -fuzzy approximation space. Properties of intuitionistic -fuzzy approximation operators are then given. This paper is devoted to discussing the relationship between intuitionistic -fuzzy relations and intuitionistic -fuzzy topologies. It proves that the set of all lower approximation sets based on a reflexive and transitive intuitionistic -fuzzy relation consists of an intuitionistic -fuzzy Alexandrov topology; and conversely, an intuitionistic -fuzzy Alexandrov topology is just the set of all lower approximation sets under a reflexive and transitive intuitionistic -fuzzy relation. That is to say, there exists a one-to-one correspondence between the set of all intuitionistic -fuzzy preorders and the set of all intuitionistic -fuzzy Alexandrov topologies.
Dissimilarity Fuzzy Soft Points and Their ApplicationsMahmood, Shuker
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2016.11.003
AbstractIn this work, we first introduce the concept of dissimilarity fuzzy soft point and study some of their properties. Some applications of dissimilarity fuzzy soft points are illustrated in decision making problems and medical diagnosis problems. Moreover, we introduce fuzzy soft limit points, fuzzy soft index and the notion of similarity measure with some of their properties studied. Some applications of similarity measure are shown in decision making problems in the ministry of planning and sustainable development as well.
A Method of Spatial Unmixing Based on Possibilistic Similarity in Soft Pattern ClassificationAlsahwa, B.; Solaiman, B.; Bossé, É.; Almouahed, S.; Guériot, D.
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2016.11.004
AbstractThis paper proposes an approach for pixel unmixing based on possibilistic similarity. The approach exploits possibilistic concepts to provide flexibility in the integration of both contextual information and a priori knowledge. Possibility distributions are first obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability density functions of different thematic classes also called endmembers. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade’s probability-possibility transformation. The pixel unmixing is then performed based on the possibilistic similarity between a local possibility distribution estimated around the considered pixel and the obtained possibility distributions representing the predefined endmembers in the analyzed image. Several possibilistic similarity measures have been tested to improve the discrimination between endmembers. Results show that the proposed approach represents an efficient estimator of the proportion of each endmember present in the pixel (abundances) and achieves higher classification accuracy. Performance analysis has been conducted using synthetic and real images.
Optimization of Industrial Wastewater Treatment Using Intuitionistic Fuzzy Goal Geometric Programming ProblemGhosh, Payel; Roy, T.K.; Majumder, Chanchal
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2016.09.002
AbstractThis paper describes a design of industrial wastewater treatment plant, operating on pulp and paper manufacturing waste. The main objective is to formulate cost effective biological treatment process on industrial wastewater. In terms of the percent of five-day bio-chemical oxygen demand removed, the required final effluent quality has been maximized. Also, the cost involved has been minimized in five-day bio-chemical oxygen demand removal. To optimize remaining five-day bio-chemical oxygen demand in wastewater spending minimum money, a goal geometric programming is used here. Moreover, goal geometric programming is described incorporating imprecision in it. A model of intuitionistic fuzzy goal geometric programming problem is given in this paper. We have solved intuitionistic fuzzy goal programming model using geometric programming technique. The algorithm of the approach is discussed in this paper. It is also shown that the usefulness of intuitive fuzzy environment in goal geometric programming is compared to fuzzy goal geometric programming using a numerical example.
A Fuzzy Mutual Information-based Feature Selection Method for ClassificationHoque, N.; Ahmed, H.A.; Bhattacharyya, D.K.; Kalita, J.K.
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2016.09.004
AbstractIn this paper, we present a feature selection method called Fuzzy Mutual Information-based Feature Selection with Non-Dominated solution (FMIFS-ND) using a fuzzy mutual information measure which selects features based on feature-class fuzzy mutual information and feature-feature fuzzy mutual information. To evaluate classification accuracy of the proposed method, a modification of the -nearest neighbor (KNN) classifier is also presented in this paper to classify instances based on the distance or similarity between individual features. The performance of both methods is evaluated on multiple UCI datasets by using four classifiers. We compare the accuracy of our feature selection method with existing feature selection methods and validate accuracy of the proposed classifier with decision trees, random forests, naive Bayes, KNN and support vector machines (SVM). Experimental results show that the feature selection method gives high classification accuracy in most high dimensional datasets as well as the accuracy of proposed classifiers outperforms the traditional KNN classifier.