Three-way Decisions with Evaluative Linguistic Expressions

We propose a linguistic interpretation of three-way decisions, where the regions of acceptance, rejection, and non-commitment are constructed by using the so-called evaluative linguistic expressions, which are expressions of natural language such as small, medium, very short, quite roughly strong, extremely good, etc. Our results highlight new connections between two different research areas: three-way decisions and the theory of evaluative linguistic expressions

An Unsupervised Three-way Decisions Framework of Overload Preference Based on Adjusted Weight Multi-attribute Decision-making Model

AbstractIn the process of traffic control, law-enforcement officials are required to accurately evaluate the potential probability of freight-driver's overloading behavior. This study establishes a model of overloading preference assessment on the basis of freight-driver's individual variation. With indexes selecting, the equal-weight and AHP-based adjusted weight decision-making model are used respectively to evaluate freight-driver's overload preference. Synthesizing the results from two models, we present a three-way decisions model to make judgment

Strategy Selection and Outcome Evaluation of Three-Way Decisions Based on Reinforcement Learning

The trisecting-acting-outcome (TAO) model of three-way decision (3WD) consists of three steps: trisect a whole, design action strategies, and outcome analysis and measurement. Currently, research on outcome evaluation aims to measure the pre- and post-change in outcomes following the implementation of strategies, and it is still unable to predict which strategy will achieve the maximum effect. To narrow down this gap, this paper focuses on the “acting” and “outcome” of the TAO model and introduces a method for strategy selection and outcome prediction for the change-based three-way decision based on Q-learning in reinforcement learning. Firstly, the approach is to treat the altered tri-partition and the acting in the change-based three-way decision TAO model as states and actions in reinforcement learning, respectively, and to consider the process of obtaining a newly altered tri-partition each time under the acting of action or strategy as a cycle. The reward generated by each cycle is calculated using cumulative prospect theory, and the interaction process between the agent and the environment is represented by a Markov decision process. Secondly, a target reward is set, and the state when the cumulative reward of each cycle reaches the target reward is taken as the termination state of the Markov decision process. Then a Q-learning algorithm is used to iterate a set of actions that achieve the target reward in the shortest cycle and then the action set is used to predict the future utility of the change-based three-way decision. Finally, an example is employed to illustrate the applicability and effectiveness of the method

NIS-Apriori-based rule generation with three-way decisions and its application system in SQL

In the study, non-deterministic information systems-Apriori-based (NIS-Apriori-based) rule generation from table data sets with incomplete information, SQL implementation, and the unique characteristics of the new framework are presented. Additionally, a few unsolved new research topics are proposed based on the framework. We follow the framework of NISs and propose certain rules and possible rules based on possible world semantics. Although each rule τ depends on a large number of possible tables, we prove that each rule τ is determined by examining only two τ -dependent possible tables. The NIS-Apriori algorithm is an adjusted Apriori algorithm that can handle such tables. Furthermore, it is logically sound and complete with regard to the rules. Subsequently, the implementation of the NIS-Apriori algorithm in SQL is described and a few new topics induced by effects of NIS-Apriori-based rule generation are confirmed. One of the topics that are considered is the possibility of estimating missing values via the obtained certain rules. The proposed methodology and the environment yielded by NIS-Apriori-based rule generation in SQL are useful for table data analysis with three-way decisions

Threat assessment of aerial targets based on improved GRA-TOPSIS method and three-way decisions

Target threat assessment is a critical aspect of information warfare and can offer valuable auxiliary support to combat command decision-making. Aiming to address the shortcomings of three decision-making methods in air combat target assessment, such as the inability to effectively handle uncertain situation information and quantitatively rank the decision-making targets according to their importance, a dynamic intuitionistic fuzzy decision model based on the improved GRA-TOPSIS method and three-way decisions is proposed. First, the target attribute weight is obtained by cosine intuitionistic fuzzy entropy algorithm; then, a novel intuitionistic fuzzy distance measure is introduced, and grey incidence analysis and TOPSIS are used to build the conditional probability for three-way decisions that fully utilize the existing information and reflect the consistency of dynamic change trend; finally, the comprehensive loss function matrix is constructed and the threat classification results are obtained using the decision rules. The example analysis shows that the proposed method can not only effectively handle complex battlefield situations and dynamic uncertain information, but it can also classify targets, improving the effectiveness and rationality of decision-making and providing a reference basis for scientific command decision-making

An adjusted Apriori algorithm to itemsets defined by tables and an improved rule generator with three-way decisions

The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a characteristic of table data sets that there is usually a fixed decision attribute. Therefore, it is enough for us to handle itemsets with one decision attribute, and we can see that one frequent itemset defines one implication. We make use of these characteristics and reduce the unnecessary itemsets for improving the performance of execution. Some experiments by the implemented software tool in Python clarify the improved performance.International Joint Conference on Rough Sets, IJCRS 2020, June 29 – July 3, 2020, Havana, Cuba (COVID-19の感染拡大によるオンライン開催に変更