Attribute Reduction Algorithm for Incomplete Information Systems Based on Intuitive Fuzzy Pairs

The current attribute reduction algorithms for information systems are difficult to handle imbalanced data with default values. Therefore, address the shortcomings of traditional (ARAs) in incomplete systems, a new algorithm is proposed by introducing intuitive fuzzy pairs (IFP). In addition, composite minority oversampling technique TampC and Central Limit SMOTE (TampC-CL-SMOTE) improve pre-data sampling method algorithm, its effectiveness verified experiments. experimental results show that average classification accuracy improved on naive Bayes classifier 82.13%, support vector machine 86.48%. comparison operational efficiency, running time 5.92 seconds, overall consumption lower than algorithm. Meanwhile, accuracy, recall, F-measure 76.14%, 78.35%, 77.19%, respectively. G-means TampC-CL-SMOTE 2.9% 5.3% higher Overall, has high efficiency handling data, while optimization practical applications advantages low environments.