Machine Learning
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A model based on ant colony system and rough set theory to feature selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Linear discriminant analysis in network traffic modelling: Research Articles
International Journal of Communication Systems
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Network intrusion detection: Evaluating cluster, discriminant, and logit analysis
Information Sciences: an International Journal
Home-PC usage and achievement in English
Computers & Education
A weighted rough set based method developed for class imbalance learning
Information Sciences: an International Journal
Tabu search for attribute reduction in rough set theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Rough set algorithm for crack category determination of reinforced concrete structures
Advances in Engineering Software
Rough set based hybrid algorithm for text classification
Expert Systems with Applications: An International Journal
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
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Rough set theory (RST) is an emerging technique used to deal with problems in data mining and knowledge acquisition. However, the RST approach has not been widely explored in the field of academic achievement. This investigation developed an improved RST (IMRST) model, which employs linear discriminant analysis to determine a reduct of RST, and analyzed the academic achievements of junior high school students in Taiwan. An interactive interface was created so that students could answer questions to predict their academic achievement and they could learn essential skills for improving their academic achievement. Empirical results showed that the IMRST model selects crucial information from data without predetermining factors and can provide accurate rates for inference rules. Hence, the developed IMRST model is a promising alternative for analyzing academic achievement data.