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Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function.
However, the classification accuracy is based on the strength and number of rules generated using rough set theory.
Using Rough Set Theory, the inherent uncertainty and vagueness in the relay event report have been resolved using the concepts of discernibility, elementary sets and set approximations.
This paper presents a novel extension to the fuzzy QFD methodology using rough set theory, with the aim to facilitate decision making in the early stages of product development and lead to the establishment of a QFD-based expert system for product design.
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In their study, Lin et al. (2011) used rough set theory and rule-based decision-making techniques to extract rules related to customer churn in credit card accounts using a flow network graph (a path-dependent approach to deriving decision rules and variables).
Sengupta et al. [29] designed an online intrusion detection system (IDS) using both rough set theory and Q-learning algorithm to achieve a maximum classification algorithm that classifies data as either normal or anomaly using NSL-KDD network traffic data.
The most important results of data analysis using the rough set theory are reduct sets and core attributes.
In the former step, the rough k-means classifier is proposed to cluster the samples of radar emitter signals by using the rough set theory.
Firstly, it uses the rough set theory to process selection and then uses the improved genetic algorithm based on clustering to find the optimal subset in the remaining subset.
We use the Rough set theory and MATLAB tool to extract a sample of data-oriented implicit risk, seek for the decision-making knowledge rule, and verify the "2-3 structure" model of quality and risk of agricultural products.
In [ 8], the authors also used rough sets for gene selection.
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