基于密度峰值聚类和局部稀疏度的过采样算法
吕佳, 郭铭

Oversampling algorithm based on density peaks clustering and local sparsity
Lü Jia, Ming Guo
表5 各算法在C4.5分类器上的各个指标评价结果
Table 5 Evaluation results of each index of each algorithm on the C4.5 classifier
DatasetsEvaluationSMOBorSafeADASSM⁃IPFK⁃m SADAPRSMDPCLSO
D1F1⁃measure95.3504%95.9219%95.0244%95.9219%89.1739%94.6911%97.0330%95.9219%95.5800%
G⁃mean96.7189%97.0969%96.2903%97.0969%93.3878%97.0969%98.1526%97.0969%97.4750%
D2F1⁃measure85.2866%80.7499%85.6621%82.5853%88.1044%82.6621%83.5422%84.0379%86.7211%
G⁃mean90.6153%87.4349%89.4995%89.1953%92.7331%88.6294%88.9781%89.0365%91.4930%
D3F1⁃measure91.0761%90.7114%89.5812%89.7583%86.9262%89.2127%91.3965%90.4775%92.4308%
G⁃mean92.4931%92.2069%91.2686%91.5019%89.0857%90.9029%92.6818%92.0207%93.5368%
D4F1⁃measure58.0147%56.5508%55.8179%52.8216%58.5686%51.9370%58.2561%58.6345%57.5804%
G⁃mean62.2464%59.6573%61.0640%55.4117%43.2818%55.9819%63.7136%62.6257%63.3409%
D5F1⁃measure50.2477%46.2616%45.2641%47.9719%50.9139%49.9714%46.9857%45.5351%49.9561%
G⁃mean63.0937%59.8204%58.9365%61.2255%63.4165%62.8464%60.1788%59.0383%62.7733%
D6F1⁃measureNaN38.3952%NaNNaN45.3598%35.6085%43.3759%NaN46.5640%
G⁃mean56.1748%55.9361%44.1640%49.1841%63.9365%53.2240%59.0591%57.1104%63.5003%
D7F1⁃measure77.7990%76.7148%81.4483%75.0648%74.3187%77.9407%77.3053%79.2532%82.6409%
G⁃mean83.3495%82.6656%85.6686%81.4911%80.7767%83.1034%83.0608%84.4409%87.2673%
D8F1⁃measure40.8288%44.8296%42.0709%44.4995%52.4160%40.5528%33.3644%38.9963%47.1016%
G⁃mean55.3644%59.9155%56.7622%59.2967%65.0128%55.4519%49.6612%54.8542%60.7536%
D9F1⁃measure91.1449%90.9919%90.9771%91.0146%89.7471%91.1402%89.7448%90.8830%91.2878%
G⁃mean93.1435%92.9086%92.9363%92.9736%92.2880%93.1267%91.8047%92.9036%93.3944%
D10F1⁃measure76.2830%75.4806%76.8681%76.6538%77.1066%76.3623%76.6250%77.4209%77.7131%
G⁃mean82.7515%81.8017%82.8221%83.1245%84.2765%82.9522%82.9646%83.6477%83.8657%
D11F1⁃measure77.2319%77.2165%76.2536%77.0151%81.0328%78.3353%78.8932%79.1148%78.8529%
G⁃mean76.7661%77.3651%76.3400%77.0226%78.2896%77.9084%79.0216%79.1874%79.1526%
D12F1⁃measure96.4695%95.9850%96.8202%96.6032%94.0345%97.1781%96.0692%97.2658%97.5309%
G⁃mean98.1752%97.3734%98.0413%98.2613%96.4397%98.0484%97.7318%98.4311%98.6106%
D13F1⁃measure64.9351%69.5000%72.8778%71.1304%74.7826%69.3333%69.5652%65.3333%72.2826%
G⁃mean66.1703%72.3171%76.1999%73.9165%75.4439%72.2677%73.2411%69.1026%76.4439%
D14F1⁃measure92.0246%92.6147%91.7111%92.1847%82.0128%90.5300%90.1884%92.0944%93.2130%
G⁃mean96.0898%96.1157%95.7903%96.1208%90.4755%95.3021%96.1252%96.1801%96.2612%
D15F1⁃measure90.4892%60.4396%74.7253%79.1667%77.5000%90.4213%96.1538%89.0110%90.5983%
G⁃mean96.1528%71.2328%81.6052%85.5086%89.4334%96.2842%96.2910%89.5923%96.2328%
D16F1⁃measure76.4519%77.4593%75.9186%76.1207%74.6716%77.0081%77.1894%74.8543%76.6652%
G⁃mean88.7605%88.4716%86.6235%88.5958%82.3485%87.5854%90.1391%87.9138%88.2057%
D17F1⁃measure95.6210%95.8070%95.9727%94.0132%94.9890%96.0063%95.5166%95.4641%96.0063%
G⁃mean96.9293%96.8651%97.0851%95.6145%96.5988%96.9495%96.8329%96.6793%96.9495%
D18F1⁃measure95.5141%95.3489%95.2893%95.3475%94.5738%95.4256%94.9416%95.5958%95.8715%
G⁃mean95.6370%95.5051%95.4224%95.5040%94.6074%95.9596%95.1048%95.7345%96.0039%