Parametric aggregation in ordered weighted averaging
International Journal of Approximate Reasoning
An outranking method for multi-criteria decision making with duplex linguistic information
Fuzzy Sets and Systems
Information Sciences: an International Journal
The ordered multiplicative modular geometric operator
Knowledge-Based Systems
Finding knowledgeable groups in enterprise corpora
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts
Knowledge-Based Systems
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Type-1 Ordered Weighted Averaging (OWA) operator provides us with a new technique for directly aggregating uncertain information with uncertain weights via OWA mechanism in soft decision making and data mining, in which uncertain objects are modeled by fuzzy sets. The Direct Approach to performing type-1 OWA operation involves high computational overhead. In this paper, we define a type-1 OWA operator based on the \alpha-cuts of fuzzy sets. Then, we prove a Representation Theorem of type-1 OWA operators, by which type-1 OWA operators can be decomposed into a series of \alpha-level type-1 OWA operators. Furthermore, we suggest a fast approach, called Alpha-Level Approach, to implementing the type-1 OWA operator. A practical application of type-1 OWA operators to breast cancer treatments is addressed. Experimental results and theoretical analyses show that: 1) the Alpha-Level Approach with linear order complexity can achieve much higher computing efficiency in performing type-1 OWA operation than the existing Direct Approach, 2) the type-1 OWA operators exhibit different aggregation behaviors from the existing fuzzy weighted averaging (FWA) operators, and 3) the type-1 OWA operators demonstrate the ability to efficiently aggregate uncertain information with uncertain weights in solving real-world soft decision-making problems.