A complex Pythagorean fuzzy set, an extension of Pythagorean fuzzy set, is a powerful tool to handle two dimension phenomenon. Dombi operators with operational parameters have outstanding flexibility. This article presents certain aggregation operators under complex Pythagorean fuzzy environment, including complex Pythagorean Dombi fuzzy weighted arithmetic averaging (CPDFWAA) operator, complex Pythagorean Dombi fuzzy weighted geometric averaging (CPDFWGA) operator, complex Pythagorean Dombi fuzzy ordered weighted arithmetic averaging (CPDFOWAA) operator and complex Pythagorean Dombi fuzzy ordered weighted geometric averaging (CPDFOWGA) operator. Moreover, this paper explores some fundamental properties of these operators with appropriate elaboration. A decision‐making numerical example related to the selection of bank to purchase loan is given to demonstrate the significance of our proposed approach. Finally, a comparative analysis with existing operators is given to demonstrate the peculiarity of our proposed operators.

No abstract

This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA-II). The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.

No abstract

Complex spherical fuzzy set, an extended version of spherical fuzzy set, is a very powerful tool to capture fourfold information (typically yes, no, abstain and refusal), in which the range of degrees occurs in the complex plane with unit disk. Through this prominent feature, complex spherical fuzzy sets outperform earlier concepts of fuzzy sets and their extensions. This research article utilizes complex spherical fuzzy sets and prioritized weighted aggregation operators to construct the complex spherical fuzzy prioritized weighted averaging/geometric operators. We present their most noticeable properties. Further, we establish a decision-making approach that takes full advantage of the aforesaid operators. To explore their superiority and applicability in decision making, we apply our algorithm to a numerical example. Finally, we compare this decision-making approach with prevailing methods in this context.