Year: 2025
Type: conference-paper
Abstract: The teaching demand data for accounting courses belongs to high-dimensional data. In high-dimensional space, the data has high sparsity and is prone to overfitting, which affects the accuracy of mining results. Therefore, this study designed a data mining method based on rough sets. After collecting preliminary data related to the teaching needs of accounting courses, cleaning and filling processing are carried out, and then a set of teaching demand attributes is constructed from two aspects: conditional attributes and decision attributes. By utilizing the strong dimensionality reduction and data reduction capabilities of rough sets, attribute sets are reduced and rules are extracted. By identifying attribute sets that have a significant impact on decision-making in the data, high-dimensional data is transformed into low dimensional, more easily processed data, and the accuracy of mining is improved by reducing the dimensionality of the data. Extract teaching demand rules from the reduced attribute table and output the required teaching demand data based on the credibility of the data. The experimental results show that this method performs well in three indicators: misclassification rate, and mining coverage, and has practical application value and promotion prospects.
Author: Man Li
Language: English
Topic: Rough Sets and Fuzzy Logic
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4411744452