{"title":"基于不平衡数据的犯罪心理归因智能评估方法","authors":"Guandong Gao , Ke Xiao , Hui Li , Shengzun Song","doi":"10.1016/j.chb.2024.108286","DOIUrl":null,"url":null,"abstract":"<div><p>Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.</p></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":null,"pages":null},"PeriodicalIF":9.0000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An intelligent assessment method of criminal psychological attribution based on unbalance data\",\"authors\":\"Guandong Gao , Ke Xiao , Hui Li , Shengzun Song\",\"doi\":\"10.1016/j.chb.2024.108286\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.</p></div>\",\"PeriodicalId\":48471,\"journal\":{\"name\":\"Computers in Human Behavior\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.0000,\"publicationDate\":\"2024-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in Human Behavior\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0747563224001547\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Human Behavior","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0747563224001547","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
犯罪案件在归因类型划分时往往表现出不平衡性,无法通过数据扩充进行扩展。为了解决罪犯归因分类中数据不平衡的问题,本文通过改进的平衡 TF-Distinguishing IDF 方法(B-TF-dIDF)提出了一种犯罪心理归因评估模型,并利用注意力方法构建了一个混合网络,将数字特征和文本特征进行融合,以提高评估的准确性。首先,作为一种统计方法,B-TF-dIDF 被提出来用于减少抽取数字特征时类别不平衡的影响,其中添加了一个平衡元素来减少错误类型关键词对分类的影响,同时添加了一个区分元素来区分关键词的类型。然后,构建一个由长短期记忆(LSTM)和卷积神经网络(CNN)组成的改进型混合网络模型,以平衡不同长度文本样本对提取犯罪文本语义特征的影响。为了根据不同特征的重要性评估其权重,使用了空间注意力来改进特征图中的 CNN。此外,还使用自我关注来重新评估混合特征。最后,softmax 分类器为进一步开发分层处理机制提供了科学依据。此外,我们还建立了一个带有真实案件标签的犯罪数据集进行测试。实验证明,所提出的模型在微观和宏观等多个评价指标上都优于其他相关方法。此外,少数样本的 F1 提高了 6%-8%,这表明提出的方法可以减少类不平衡的影响。
An intelligent assessment method of criminal psychological attribution based on unbalance data
Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.
期刊介绍:
Computers in Human Behavior is a scholarly journal that explores the psychological aspects of computer use. It covers original theoretical works, research reports, literature reviews, and software and book reviews. The journal examines both the use of computers in psychology, psychiatry, and related fields, and the psychological impact of computer use on individuals, groups, and society. Articles discuss topics such as professional practice, training, research, human development, learning, cognition, personality, and social interactions. It focuses on human interactions with computers, considering the computer as a medium through which human behaviors are shaped and expressed. Professionals interested in the psychological aspects of computer use will find this journal valuable, even with limited knowledge of computers.