Decision-theoretic rough set model of multi-source decision systems

Sang, Binbin; Guo, Yanting; Shi, Derong; Xu, Weihua

doi:10.1007/s13042-017-0729-x

Cited by 42 publications

(7 citation statements)

References 37 publications

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“…The boundary domain is an empty set if d R (X) = 1 and it is definable. X has a non-empty boundary domain if d R (X) < 1 and it is undefinable [16,17].…”

Section: Rough Setmentioning

confidence: 99%

Rough Set-Game Theory Information Mining Model Considering Opponents’ Information

et al. 2022

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In multi-strategy games, the increase in the number of strategies makes it difficult to make a solution. To maintain the competition advantage and obtain maximal profits, one side of the game hopes to predict the opponent’s behavior. Building a model to predict an opponent’s behavior is helpful. In this paper, we propose a rough set-game theory model (RS-GT) considering uncertain information and the opponent’s decision rules. The uncertainty of strategies is obtained based on the rough set method, and an accurate solution is obtained based on game theory from the rough set-game theory model. The players obtain their competitors’ decision rules to predict the opponents’ behavior by mining the information from repeated games in the past. The players determine their strategy to obtain maximum profits by predicting the opponent’s actions, i.e., adopting a first-mover or second-mover strategy to build a favorable situation. The result suggests that the rough set-game theory model helps enterprises avoid unnecessary losses and allows them to obtain greater profits.

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“…The boundary domain is an empty set if d R (X) = 1 and it is definable. X has a non-empty boundary domain if d R (X) < 1 and it is undefinable [16,17].…”

Section: Rough Setmentioning

confidence: 99%

Rough Set-Game Theory Information Mining Model Considering Opponents’ Information

et al. 2022

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“…The trisecting-acting-outcome (TAO) model [28] of thinking in threes [29] to understand and process a whole via three distinct and related parts [30] has inspired many novel and significant theories and applications. Recently, theories such as three-way formal concept analysis [31] and three-way cognition computing [32,33] have focused on concept learning via multi-granularity from the viewpoint of cognition. The three-way fuzzy sets method [34], three-way decisions space [35], sequential three-way decisions [36], and generalized three-way decision models [37][38][39] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series

et al. 2021

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Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

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“…Rough set theory was founded in the 1980s by Pawlak [1], a professor of Warsaw University of Technology and academician of the Polish Academy of Sciences. After more than 30 years of rapid development, a relatively complete theoretical system has been formed [2–7], and a very rich application research results have been achieved. In particular, compared with other methods, a rough set theory is more suitable for dealing with uncertainties contained in information systems.…”

Section: Introductionmentioning

confidence: 99%