LASSO-based feature selection and naïve Bayes classifier for crime prediction and its type

Nitta, Gnaneswara Rao; Rao, B. Yogeshwara; Sravani, T.; Ramakrishiah, N.; BalaAnand, M.

doi:10.1007/s11761-018-0251-3

Cited by 31 publications

(16 citation statements)

References 10 publications

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“…After 6000 pieces of data, the imported data contains some data that do not conform to objective laws, and it is used as sample data for machine learning. As can be seen from Figure 6 , the accuracy of literature [ 20 ], literature [ 21 ], literature [ 22 ] and the prediction model in this paper all declined when the data volume was 7000, but the accuracy still maintained a high level.…”

Section: Results Analysis and Discussionmentioning

confidence: 80%

An Improved Transition Probability Matrix for Crime Distribution Prediction

Zhang

2022

Computational Intelligence and Neuroscience

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The occurrence of crime has always been the main problem affecting urban public security and social security environment. Therefore, the prevention and control of crime is the focus of public security work. The traditional police strategy has poor timeliness and cannot respond and adjust in real time with the occurrence of criminal activities, and its deterrence and control of criminal activities are limited. To address the problem of low accuracy of existing crime distribution prediction models, an improved transition probability matrix for crime distribution prediction is proposed in this paper. Based on a large number of trajectory data of criminals, this paper quantitatively describes the temporal and spatial characteristics of crowd movement in different areas of the city by using the temporal and spatial transfer probability. Then, combining Markov chain and Bayes’ theorem, the probability model of spatio-temporal transfer of criminal groups in regions is constructed. Finally, the model predicts the number of crimes in urban grid areas.

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Section: Results Analysis and Discussionmentioning

confidence: 80%

An Improved Transition Probability Matrix for Crime Distribution Prediction

Zhang

2022

Computational Intelligence and Neuroscience

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“…Since the subject is significant, many machine learning and data mining studies are also carried out in this field. Prediction of crime cases [1][2][3], estimation of the crime type based on crime features [4], prediction of the identity of the suspect [5][6], forecasting the spatial distribution of crime events [7] are some of these study areas, where basic tasks of data mining, i.e. classification, clustering, regression, and association rule mining techniques, were applied.…”

Section: Prediction Of Crime Occurrence In Case Of Scarcity Of Labeled Data Etiketlenmiş Verilerin Kıtlığı Durumunda Suç Oluşumunun Tahmimentioning

confidence: 99%

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

Kiranoglu¹,

Tuysuzoglu²,

Kiyak³

2021

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis. The prediction of crime occurrences, the detection of the spatial/temporal distribution of the criminal cases, forecasting the type of crime are some of these study areas. By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily. Therefore, in this study, active learning, selflearning, and random sampling techniques are applied to predict the outcome of criminal searches in England using the police data of 2019. According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.

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“…Based on the above excellent properties, the naive Bayesian classification algorithm has a wide range of applications, such as clinical medicine [1][2][3], telecommunications [4,5], artificial intelligence [6], linguistics [7,8], gene technology [9], precision instruments [10], and other fields. At the same time, naive Bayes classification algorithm has strong compatibility, which can form more powerful algorithms when combined with other methods, such as double-weighted fuzzy gamma naive Bayes classification [11], fuzzy association naive Bayes classification [12], complex network naive Bayes classification [13], feature selection naive Bayes classification [14], tree augmented naive Bayes classification [15], etc.…”

Section: Introductionmentioning

confidence: 99%

Improved naive Bayes classification algorithm for traffic risk management

Chen

Hua

et al. 2021

EURASIP J. Adv. Signal Process.

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Naive Bayesian classification algorithm is widely used in big data analysis and other fields because of its simple and fast algorithm structure. Aiming at the shortcomings of the naive Bayes classification algorithm, this paper uses feature weighting and Laplace calibration to improve it, and obtains the improved naive Bayes classification algorithm. Through numerical simulation, it is found that when the sample size is large, the accuracy of the improved naive Bayes classification algorithm is more than 99%, and it is very stable; when the sample attribute is less than 400 and the number of categories is less than 24, the accuracy of the improved naive Bayes classification algorithm is more than 95%. Through empirical research, it is found that the improved naive Bayes classification algorithm can greatly improve the correct rate of discrimination analysis from 49.5 to 92%. Through robustness analysis, the improved naive Bayes classification algorithm has higher accuracy.

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LASSO-based feature selection and naïve Bayes classifier for crime prediction and its type

Cited by 31 publications

References 10 publications

An Improved Transition Probability Matrix for Crime Distribution Prediction

An Improved Transition Probability Matrix for Crime Distribution Prediction

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

Improved naive Bayes classification algorithm for traffic risk management

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