Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 403-410.DOI: 10.11772/j.issn.1001-9081.2023030270
• Artificial intelligence • Previous Articles
Jiawei ZHANG1,2, Guandong GAO2,3(), Ke XIAO1,4, Shengzun SONG5
Received:
2023-03-16
Revised:
2023-05-09
Accepted:
2023-05-11
Online:
2023-05-23
Published:
2024-02-10
Contact:
Guandong GAO
About author:
ZHANG Jiawei, born in 1998, M. S. candidate. His research interests include natural language processing, criminal psychological portrait.Supported by:
通讯作者:
高冠东
作者简介:
张家伟(1998—),男,河北邯郸人,硕士研究生,主要研究方向:自然语言处理、犯罪心理画像基金资助:
CLC Number:
Jiawei ZHANG, Guandong GAO, Ke XIAO, Shengzun SONG. Violent crime hierarchy algorithm by joint modeling of improved hierarchical attention network and TextCNN[J]. Journal of Computer Applications, 2024, 44(2): 403-410.
张家伟, 高冠东, 肖珂, 宋胜尊. 基于改进分层注意网络和TextCNN联合建模的暴力犯罪分级算法[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 403-410.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023030270
参数 | 参数值 |
---|---|
Kernels size | 3,4,5 |
Number of kernels | 128 |
Number of Bi-GRU Layers | 1 |
Number of Bi-GRU hidden units | 64 |
Batch size | 128 |
Epoch | 50 |
Loss function | Focal Loss |
Learning rate | 0.001 |
Tab. 1 Hyperparameter setting
参数 | 参数值 |
---|---|
Kernels size | 3,4,5 |
Number of kernels | 128 |
Number of Bi-GRU Layers | 1 |
Number of Bi-GRU hidden units | 64 |
Batch size | 128 |
Epoch | 50 |
Loss function | Focal Loss |
Learning rate | 0.001 |
类型 | 训练集样本数 | 验证集样本数 | 测试集样本数 | 总计 |
---|---|---|---|---|
总计 | 2 800 | 933 | 932 | 4 665 |
胆汁质 | 1 340 | 446 | 446 | 2 232 |
多血质 | 1 178 | 393 | 392 | 1 963 |
粘液质 | 279 | 93 | 93 | 465 |
抑郁质 | 3 | 1 | 1 | 5 |
Tab. 2 Dataset division
类型 | 训练集样本数 | 验证集样本数 | 测试集样本数 | 总计 |
---|---|---|---|---|
总计 | 2 800 | 933 | 932 | 4 665 |
胆汁质 | 1 340 | 446 | 446 | 2 232 |
多血质 | 1 178 | 393 | 392 | 1 963 |
粘液质 | 279 | 93 | 93 | 465 |
抑郁质 | 3 | 1 | 1 | 5 |
类型 | 模型 | Acc | Macro_P | Macro_R | Macro_F1 | Macro_AUC | Micro_P | Micro_R | Micro_F1 | Micro_AUC |
---|---|---|---|---|---|---|---|---|---|---|
犯罪事实 语义建模 | HAN | 90.45 | 64.72 | 67.87 | 66.05 | 86.89 | 90.45 | 90.45 | 90.45 | 97.40 |
HAN+Focal Loss | 91.52 | 67.68 | 69.70 | 68.61 | 88.77 | 91.52 | 91.52 | 91.52 | 98.72 | |
HAN+Focal Loss+位置编码 | 92.60 | 67.74 | 68.87 | 68.27 | 94.48 | 92.60 | 92.60 | 92.60 | 98.88 | |
HAN+Focal Loss+位置编码+显著向量 | 96.57 | 71.82 | 72.14 | 71.96 | 97.88 | 96.57 | 96.57 | 96.57 | 99.61 | |
HAN+Focal Loss+位置编码+ 显著向量+GAP | 97.10 | 72.69 | 71.79 | 72.21 | 98.09 | 97.10 | 97.10 | 97.10 | 99.51 | |
犯罪基本 情况语义 建模 | TextCNN | 92.60 | 69.33 | 68.62 | 68.96 | 85.52 | 92.60 | 92.60 | 92.60 | 99.10 |
TextCNN+Focal Loss | 93.45 | 70.92 | 69.81 | 70.30 | 89.48 | 93.45 | 93.45 | 93.45 | 99.42 | |
TextCNN+Focal Loss+位置编码 | 94.21 | 71.05 | 69.12 | 70.00 | 94.13 | 94.21 | 94.21 | 94.21 | 99.52 | |
TextCNN+Focal Loss+位置编码+GAP | 95.17 | 71.65 | 71.17 | 71.38 | 94.32 | 95.17 | 95.17 | 95.17 | 99.38 | |
联合建模 | CCHA-Net | 99.57 | 74.75 | 74.62 | 74.68 | 99.45 | 99.57 | 99.57 | 99.57 | 99.89 |
Tab. 3 Test results of ablation experiments
类型 | 模型 | Acc | Macro_P | Macro_R | Macro_F1 | Macro_AUC | Micro_P | Micro_R | Micro_F1 | Micro_AUC |
---|---|---|---|---|---|---|---|---|---|---|
犯罪事实 语义建模 | HAN | 90.45 | 64.72 | 67.87 | 66.05 | 86.89 | 90.45 | 90.45 | 90.45 | 97.40 |
HAN+Focal Loss | 91.52 | 67.68 | 69.70 | 68.61 | 88.77 | 91.52 | 91.52 | 91.52 | 98.72 | |
HAN+Focal Loss+位置编码 | 92.60 | 67.74 | 68.87 | 68.27 | 94.48 | 92.60 | 92.60 | 92.60 | 98.88 | |
HAN+Focal Loss+位置编码+显著向量 | 96.57 | 71.82 | 72.14 | 71.96 | 97.88 | 96.57 | 96.57 | 96.57 | 99.61 | |
HAN+Focal Loss+位置编码+ 显著向量+GAP | 97.10 | 72.69 | 71.79 | 72.21 | 98.09 | 97.10 | 97.10 | 97.10 | 99.51 | |
犯罪基本 情况语义 建模 | TextCNN | 92.60 | 69.33 | 68.62 | 68.96 | 85.52 | 92.60 | 92.60 | 92.60 | 99.10 |
TextCNN+Focal Loss | 93.45 | 70.92 | 69.81 | 70.30 | 89.48 | 93.45 | 93.45 | 93.45 | 99.42 | |
TextCNN+Focal Loss+位置编码 | 94.21 | 71.05 | 69.12 | 70.00 | 94.13 | 94.21 | 94.21 | 94.21 | 99.52 | |
TextCNN+Focal Loss+位置编码+GAP | 95.17 | 71.65 | 71.17 | 71.38 | 94.32 | 95.17 | 95.17 | 95.17 | 99.38 | |
联合建模 | CCHA-Net | 99.57 | 74.75 | 74.62 | 74.68 | 99.45 | 99.57 | 99.57 | 99.57 | 99.89 |
类型 | 模型 | Acc | Macro_P | Macro_R | Macro_F1 | Macro_AUC | Micro_P | Micro_R | Micro_F1 | Micro_AUC |
---|---|---|---|---|---|---|---|---|---|---|
传统机器 学习模型 | KNN | 83.49 | 66.31 | 58.16 | 60.94 | 80.92 | 83.49 | 83.49 | 83.49 | 94.50 |
MNB | 90.46 | 69.88 | 65.02 | 66.93 | 88.46 | 90.46 | 90.46 | 90.46 | 98.69 | |
GNB | 81.41 | 92.72 | ||||||||
BNB | 89.39 | 69.74 | 58.18 | 60.88 | 86.96 | 89.39 | 89.39 | 89.39 | 98.56 | |
DT | 88.32 | 67.80 | 64.47 | 65.89 | 88.32 | 88.32 | 88.32 | |||
RF | 90.35 | 69.76 | 66.48 | 67.85 | 92.32 | 90.35 | 90.35 | 90.35 | 98.80 | |
SVM | 95.28 | 72.16 | 71.53 | 71.79 | 90.15 | 95.28 | 95.28 | 95.28 | 99.50 | |
XGBoost | 93.78 | 71.06 | 65.87 | 67.83 | 81.41 | 93.78 | 93.78 | 93.78 | 98.77 | |
LR | 95.18 | 72.32 | 71.24 | 71.72 | 93.11 | 95.18 | 95.18 | 95.18 | 99.51 | |
深度学习 模型 | LSTM | 86.71 | ||||||||
Bi-LSTM | 85.41 | 59.09 | 63.55 | 60.40 | 81.35 | 85.41 | 85.41 | 85.41 | 94.89 | |
GRU | 88.84 | 62.28 | 65.13 | 63.46 | 88.84 | 88.84 | 88.84 | 96.84 | ||
Bi-GRU | 91.31 | 65.41 | 64.62 | 64.95 | 92.93 | 91.31 | 91.31 | 91.31 | 96.84 | |
Att-BiLSTM | 92.17 | 69.77 | 70.02 | 69.77 | 85.71 | 92.17 | 92.17 | 92.17 | 98.27 | |
C-LSTM | 93.78 | 71.38 | 70.47 | 70.82 | 81.36 | 93.78 | 93.78 | 93.78 | 98.34 | |
CNN-BiLSTM | 94.53 | 72.02 | 71.09 | 71.45 | 89.81 | 94.53 | 94.53 | 94.53 | 98.40 | |
AC-BiLSTM | 95.49 | 71.60 | 71.57 | 93.86 | 95.49 | 95.49 | 95.49 | 99.15 | ||
本文模型 | CCHA-Net | 99.57 | 74.75 | 74.62 | 74.68 | 99.45 | 99.57 | 99.57 | 99.57 | 99.89 |
Tab. 4 Test results of comparative experiments
类型 | 模型 | Acc | Macro_P | Macro_R | Macro_F1 | Macro_AUC | Micro_P | Micro_R | Micro_F1 | Micro_AUC |
---|---|---|---|---|---|---|---|---|---|---|
传统机器 学习模型 | KNN | 83.49 | 66.31 | 58.16 | 60.94 | 80.92 | 83.49 | 83.49 | 83.49 | 94.50 |
MNB | 90.46 | 69.88 | 65.02 | 66.93 | 88.46 | 90.46 | 90.46 | 90.46 | 98.69 | |
GNB | 81.41 | 92.72 | ||||||||
BNB | 89.39 | 69.74 | 58.18 | 60.88 | 86.96 | 89.39 | 89.39 | 89.39 | 98.56 | |
DT | 88.32 | 67.80 | 64.47 | 65.89 | 88.32 | 88.32 | 88.32 | |||
RF | 90.35 | 69.76 | 66.48 | 67.85 | 92.32 | 90.35 | 90.35 | 90.35 | 98.80 | |
SVM | 95.28 | 72.16 | 71.53 | 71.79 | 90.15 | 95.28 | 95.28 | 95.28 | 99.50 | |
XGBoost | 93.78 | 71.06 | 65.87 | 67.83 | 81.41 | 93.78 | 93.78 | 93.78 | 98.77 | |
LR | 95.18 | 72.32 | 71.24 | 71.72 | 93.11 | 95.18 | 95.18 | 95.18 | 99.51 | |
深度学习 模型 | LSTM | 86.71 | ||||||||
Bi-LSTM | 85.41 | 59.09 | 63.55 | 60.40 | 81.35 | 85.41 | 85.41 | 85.41 | 94.89 | |
GRU | 88.84 | 62.28 | 65.13 | 63.46 | 88.84 | 88.84 | 88.84 | 96.84 | ||
Bi-GRU | 91.31 | 65.41 | 64.62 | 64.95 | 92.93 | 91.31 | 91.31 | 91.31 | 96.84 | |
Att-BiLSTM | 92.17 | 69.77 | 70.02 | 69.77 | 85.71 | 92.17 | 92.17 | 92.17 | 98.27 | |
C-LSTM | 93.78 | 71.38 | 70.47 | 70.82 | 81.36 | 93.78 | 93.78 | 93.78 | 98.34 | |
CNN-BiLSTM | 94.53 | 72.02 | 71.09 | 71.45 | 89.81 | 94.53 | 94.53 | 94.53 | 98.40 | |
AC-BiLSTM | 95.49 | 71.60 | 71.57 | 93.86 | 95.49 | 95.49 | 95.49 | 99.15 | ||
本文模型 | CCHA-Net | 99.57 | 74.75 | 74.62 | 74.68 | 99.45 | 99.57 | 99.57 | 99.57 | 99.89 |
模型 | 浮点运算量/FLOPs | 参数量/103 |
---|---|---|
TextCNN通道 | 3.506 | 196.992 |
HAN通道 | 41.410 | 62.208 |
CCHA-Net | 44.916 | 259.200 |
Tab. 5 Complexity evaluation results of dual-channel processing method
模型 | 浮点运算量/FLOPs | 参数量/103 |
---|---|---|
TextCNN通道 | 3.506 | 196.992 |
HAN通道 | 41.410 | 62.208 |
CCHA-Net | 44.916 | 259.200 |
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