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Table 7 F1-score results (%) over all datasets using all the scenarios

From: Deep reinforcement learning for data-efficient weakly supervised business process anomaly detection

Data

Proposed model

Denoising Autoencoder [21]

Variational autoencoder [22]

BINET [5]

Bi-LSTM VAE [23]

Process mining model

Datasets

Scenarios

DS1

All

97.41 ± 0.00

83.12 ± 0.18

85.09 ± 0.12

90.30 ± 0.08

91.22 ± 0.08

89.00

Rework

96.66 ± 0.07

81.58 ± 0.03

83.10 ± 0.18

88.50 ± 0.05

89.30 ± 0.02

86.85

Skip

95.47 ± 0.01

79.19 ± 0.06

81.34 ± 0.02

85.91 ± 0.01

86.84 ± 0.06

84.10

Switch

92.51 ± 0.12

68.84 ± 0.18

70.63 ± 0.02

76.74 ± 0.03

77.40 ± 0.01

75.68

DS2

All

95.60 ± 0.07

80.69 ± 0.08

83.15 ± 0.04

87.42 ± 0.17

90.46 ± 0.08

86.12

Rework

92.22 ± 0.05

79.71 ± 0.02

81.32 ± 0.03

86.64 ± 0.08

88.49 ± 0.12

84.91

Skip

91.67 ± 0.07

78.22 ± 0.04

79.47 ± 0.06

82.99 ± 0.07

86.98 ± 0.01

83.23

Switch

88.06 ± 0.05

54.93 ± 0.17

66.89 ± 0.12

74.86 ± 0.18

75.58 ± 0.15

72.70

BPIC12

All

87.56 ± 0.01

54.23 ± 0.12

55.97 ± 0.06

61.38 ± 0.05

72.11 ± 0.12

71.23

Rework

85.84 ± 0.14

52,00 ± 0.14

52.45 ± 0.01

59.30 ± 0.14

69.91 ± 0.18

68.89

Skip

83.12 ± 0.08

49.87 ± 0.18

50.66 ± 0.03

56.89 ± 0.05

67.44 ± 0.01

66.68

Switch

80.39 ± 0.02

40.20 ± 0.03

41.71 ± 0.18

45.43 ± 0.08

57.50 ± 0.02

56.74

BPIC17

All

91.40 ± 0.05

57.51 ± 0.15

58.54 ± 0.06

64.76 ± 0.12

75.63 ± 0.05

73.64

Rework

89.98 ± 0.07

55.14 ± 0.02

56.86 ± 0.07

63.94 ± 0.04

72.95 ± 0.08

70.87

Skip

89.03 ± 0.01

52.67 ± 0.05

54.43 ± 0.12

60.46 ± 0.02

69.75 ± 0.03

67.44

Switch

86.78 ± 0.12

42.29 ± 0.03

46.95 ± 0.01

50.21 ± 0.03

58.42 ± 0.01

49.26