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- W2895830510 abstract "Summary Attribute importance measurement is the core of multi‐attribute decision making. This study aims at resolving the problem of attribute importance computation. Based on the data system and the inclusion degree of sets, we propose a data deletion method based on knowledge reliability. By taking the hidden knowledge in the data system as the carrier, we further discuss the changing rules of knowledge factors in the subsystems and propose an attribute importance measurement method based on the data effect (abbreviated DE‐AIM). Furthermore, we analyze the values and structure features of DE‐AIM through a theoretical proof and an example calculation. Finally, we compare DE‐AIM with other attribute importance measurements by combining with concrete cases and several data sets from UCI. All the results indicate that DE‐AIM not only has good structural features and interpretability but also can reflect different decision preference by changing parameters. The method has wide applications in many fields such as resource management, artificial intelligence, complex system optimization, expert systems, and concurrency computation." @default.
- W2895830510 created "2018-10-26" @default.
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- W2895830510 date "2018-10-18" @default.
- W2895830510 modified "2023-10-14" @default.
- W2895830510 title "Attribute importance measurement based on the data effect and its performance analysis in computation practice" @default.
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- W2895830510 doi "https://doi.org/10.1002/cpe.5038" @default.
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