混合特征数据的自解释归约建模方法*
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江思伟, 谢振平, 陈梅婕, 蔡明
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Self-Explainable Reduction Method for Mixed Feature Data Modeling
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Jiang Siwei,Xie Zhenping,Chen Meijie,Cai Ming
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表2 不同方法的分类精度结果 |
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数据集 | Na?veBayes | 本文方法+Na?veBayes | K-means+Na?veBayes | FCM+Na?veBayes | glass | 49.53 % | 59.14±4.60% | 61.92±4.12% | 62.43±3.75% | wine-quality white | 61.55% | 67.97±3.31% | 66.14±2.99% | 65.87±3.32% | wine-quality red | 55.35% | 54.00±2.03% | 58.16±0.98% | 58.38±0.95% | dermatology | 96.99% | 96.94±0.11% | 96.83±0.18% | 96.78±0.11% | ionosphere | 82.62 % | 86.85±1.70% | 84.63±1.70% | 84.47±1.63% | adult | 88.69% | 92.47±0.82% | 89.78±0.63 % | 91.34±0.80% |
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