Mixed-attitude three-way decision model for aerial targets: Threat assessment based on IF-VIKOR-GRA method

Assessing potential threats typically necessitates the use of a robust mathematical model, a comprehensive evaluation method and universal decision rules. A novel approach is utilized in this study to optimize existing threat assessment (TA) algorithms and three-way decision models (3WDMs) are leveraged that incorporate decision-theoretic rough sets (DTRSs) within dynamic intuitionistic fuzzy (IF) environments to create a mixed-attitude 3WDM based on the IF-VIKOR-GRA method in the context of aviation warfare. The primary objectives of this study include determining conditional probabilities for IF three-way decisions (3WDs) and establishing mixed-attitude decision thresholds. Both the target attribute and loss function are expressed in the form of intuitionistic fuzzy numbers (IFNs). To calculate these conditional probabilities, an IF technique is used to combine the multi-attribute decision-making (MADM) method VIKOR and the grey relational analysis (GRA) method, while also taking into account the risk-related preferences of decision-makers (DMs). Optimistic and pessimistic 3WDMs are developed from the perspectives of membership degree and non-membership degree, then subsequently integrated into the comprehensive mixed-attitude 3WDM. The feasibility and effectiveness of this methodology are demonstrated through a numerical example and by comparison to other existing approaches

Active Sample Selection Based Incremental Algorithm for Attribute Reduction with Rough Sets

Attribute reduction with rough sets is an effective technique for obtaining a compact and informative attribute set from a given dataset. However, traditional algorithms have no explicit provision for handling dynamic datasets where data present themselves in successive samples. Incremental algorithms for attribute reduction with rough sets have been recently introduced to handle dynamic datasets with large samples, though they have high complexity in time and space. To address the time/space complexity issue of the algorithms, this paper presents a novel incremental algorithm for attribute reduction with rough sets based on the adoption of an active sample selection process and an insight into the attribute reduction process. This algorithm first decides whether each incoming sample is useful with respect to the current dataset by the active sample selection process. A useless sample is discarded while a useful sample is selected to update a reduct. At the arrival of a useful sample, the attribute reduction process is then employed to guide how to add and/or delete attributes in the current reduct. The two processes thus constitute the theoretical framework of our algorithm. The proposed algorithm is finally experimentally shown to be efficient in time and space.This is a manuscript of the publication Yang, Yanyan, Degang Chen, and Hui Wang. "Active Sample Selection Based Incremental Algorithm for Attribute Reduction With Rough Sets." IEEE Transactions on Fuzzy Systems 25, no. 4 (2017): 825-838. DOI: 10.1109/TFUZZ.2016.2581186. Posted with permission.</p