时序基因驱动的特征表示模型
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黄建平,陈可,张建松,沈思琪
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Time-series gene driven feature representation model
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Jian-ping HUANG,Ke CHEN,Jian-song ZHANG,Si-qi SHEN
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表 4 采用不同方法对地震和WebTraffic数据集的分类性能 |
Tab.4 Classification performance on earthquake and WebTraffic datasets with different methods |
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% | 数据集 | 方法 | A | | 数据集 | 方法 | A | 地震 | NN-ED | 68.22 | | WebTraffic | NN-ED | 73.40 | 地震 | NN-DTW | 70.31 | WebTraffic | NN-DTW | 74.03 | 地震 | NN-CID | 69.41 | WebTraffic | NN-CID | 74.26 | 地震 | FS | 74.66 | WebTraffic | FS | 73.89 | 地震 | TSF | 74.67 | WebTraffic | TSF | 75.38 | 地震 | SAX-VSM | 73.76 | WebTraffic | SAX-VSM | 74.91 | 地震 | MC-DCNN | 70.29 | WebTraffic | MC-DCNN | 75.29 | 地震 | LSTM | 68.35 | WebTraffic | LSTM | 73.15 | 地震 | CVAE | 74.82 | WebTraffic | CVAE | 75.17 | 地震 | GeNE | 75.54 | WebTraffic | GeNE | 75.91 |
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