3: rough set theory application in online course satisfaction, e-learning for employees, middle school students creativity assessment with neural networks.
What does RST stand for?
RST stands for Rough Set Theory
This definition appears frequently and is found in the following Acronym Finder categories:
- Science, medicine, engineering, etc.
See other definitions of RST
We have 206 other meanings of RST in our Acronym Attic
- Road Surface Temperature
- Road Surface Tester
- Robust Sequence Tree
- Rochester, MN, USA - Rochester International (Airport Code)
- Rockalicious Stone Tang
- Rocket Science Tutors, Inc. (California)
- Romance Standard Time
- Romanian Security Team (computer hacker forum)
- Rosebud Sioux Tribal (Indian tribe; South Dakota)
- Rosetta Stone, Inc. (software company; Virginia)
- Rubber Stamp Tapestry (Seagrove, NC)
- Runescape Trivia
- Russian Segment Trainer
- Reconnaissance, Surveillance and Targeting - Vehicle (US Military HMMWV replacement concept vehicle)
- Recinto Santo Tomás de Aquino
- Reconnaissance, Surveillance, and Target Acquisition (US DoD)
- Reston Swim Team Association
- Rindge School of Technical Arts
- Road Safety and Transport Authority (Bhutan)
- Rockford Science and Technology Academy
Samples in periodicals archive:
The main constituent of soft computing is rough set theory applied in the intelligent data mining.
Section 4 presents reviews of the use of rough set theory and the multi-level grid scheduler.
Designers explore such aspects of it as methods of kansei/affective engineering and specific cases of kansei products, psychophysiological methods, soft computing systems, rough set theory, the European fast-moving consumer goods industry, powered hand tools, and quality and quality function deployment.
Abstract: Rough Set Theory (RST) is a technique for data analysis.
The papers are organized by session, with topics that include rough set theory, particle swarm optimization, natural language processing, expert and decision support systems, and several sessions on applications.
From this background, the book introduces an original approach to feature selection using conventional rough set theory, then proposes a fundamental approach based on fuzzy-rough sets.
The final three chapters on traditional data mining algorithms explore the Dempster-Shafer theory for handling imperfect data, self-organized maps for outlier detection, and rough set theory for estimating error rates.