时序基因驱动的特征表示模型

黄建平,陈可,张建松,沈思琪

Time-series gene driven feature representation model

Jian-ping HUANG,Ke CHEN,Jian-song ZHANG,Si-qi SHEN

表 5 采用不同方法对MCE、INS、TMP数据集的分类性能

Tab.5 Classification performance on MCE, INS, TMP datasets with different methods

% 数据集 方法 P R F1 F0.5 MCE NN-ED 59.90 34.82 44.01 52.38 MCE NN-DTW 60.17 41.41 49.04 55.15 MCE NN-CID 57.12 40.86 47.55 52.93 MCE FS 54.34 43.54 48.34 51.74 MCE TSF 76.80 52.61 62.50 70.30 MCE SAX-VSM 65.12 59.96 62.44 64.01 MCE MC-DCNN 78.94 49.27 60.70 70.43 MCE LSTM 79.69 53.56 64.10 72.58 MCE CVAE 77.92 54.12 64.32 72.02 MCE GeNE 80.33 58.17 67.45 74.61 INS NN-ED 28.51 19.33 23.01 26.01 INS NN-DTW 27.14 21.73 24.13 25.84 INS NN-CID 52.65 10.25 17.05 28.75 INS FS 31.66 16.73 21.84 26.85 INS TSF 48.11 21.04 29.13 38.20 INS SAX-VSM 62.71 28.41 40.11 50.51 INS MC-DCNN 53.77 5.79 10.38 20.06 INS LSTM 60.25 28.01 38.23 48.93 INS CVAE 63.27 26.78 37.57 49.67 INS GeNE 71.50 33.15 45.34 58.01 TMP NN-ED 54.43 47.88 50.95 52.92 TMP NN-DTW 51.95 52.43 52.14 52.04 TMP NN-CID 56.12 49.26 52.44 54.61 TMP FS 65.17 58.82 61.85 63.76 TMP TSF 54.20 60.94 57.42 55.47 TMP SAX-VSM 72.22 59.05 64.94 69.10 TMP MC-DCNN 76.79 66.13 71.06 74.37 TMP LSTM 56.21 53.15 54.63 55.69 TMP CVAE 74.86 59.22 66.14 71.15 TMP GeNE 80.23 64.57 71.55 76.51