C4.5: programs for machine learning
C4.5: programs for machine learning
An Introduction to Neural Networks
An Introduction to Neural Networks
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Soft computing system for bank performance prediction
Applied Soft Computing
Comparison of descriptor spaces for chemical compound retrieval and classification
Knowledge and Information Systems
Boosting text segmentation via progressive classification
Knowledge and Information Systems
(Self-)Evaluation of computer competence: How gender matters
Computers & Education
Expert Systems with Applications: An International Journal
Encoding and decoding the knowledge of association rules over SVM classification trees
Knowledge and Information Systems
Using IT to assess IT: Towards greater authenticity in summative performance assessment
Computers & Education
Incremental rule induction based on rough set theory
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
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Education is recognized as the key to individual success. Particularly, elementary education is vital for providing students with basic literacy and numeracy, as well as establishing foundations in mathematics, language, science, geography, history, and other social sciences. Mathematics is fundamental to numerous fields with real life applications, including natural science, engineering, medicine, and social sciences; therefore, student mathematics-learning achievement (MLA) in elementary school is valuable. This study aims to eliminate wastage of educational resources and seek suitable hybrid models for application to education. This study proposes an integrated hybrid model based on rough set classifiers and multiple regression analysis and provides a new trial of such a hybrid model to process MLA problems for elementary schools and their teachers. The proposed model is illustrated by examining a dataset from an elementary school in Taiwan. The experimental results reveal that the proposed model outperforms the listing methods in both classification accuracy and standard deviation. The rough set LEM2 (Learning from Examples Module, version 2) algorithm generates a set of comprehensible decision rules that can be applied in a knowledge-based education system designed for interested parties. Consequently, the analytical results have important implications for strategies related to mathematics teaching/learning and support to achieve teaching goals related to mathematics education.