Virtual Laboratory Teaching Quality Evaluation Model Based on Rough Set and Support Vector Machine
نویسنده
چکیده
Virtual laboratory teaching quality evaluation helps to realize scientific teaching management. Virtual laboratory teaching quality evaluation is multi-level and multi-objective system engineering. In this paper, a teaching quality evaluation model based on rough set (RS) and on improved Binary-Tree and multicategory support vector machine (SVM) was provided. Firstly, the attribute reduction of RS was applied as a preprocessor to delete redundant attributes and conflicting objects without losing efficient information. Then an improved multi-category SVM based on Binary-Tree classification model was built to make a forecast. Finally, the model was applied to validation. Results show that this model performs well both in data classification accuracy and predictive accuracy with wide applicability.
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