An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach
Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and...
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9410543/ |
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| author | Sapna Singh Kshatri Deepak Singh Bhavana Narain Surbhi Bhatia Mohammad Tabrez Quasim G. R. Sinha |
| author_facet | Sapna Singh Kshatri Deepak Singh Bhavana Narain Surbhi Bhatia Mohammad Tabrez Quasim G. R. Sinha |
| author_sort | Sapna Singh Kshatri |
| collection | DOAJ |
| description | Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naïve byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier. |
| first_indexed | 2024-12-17T05:16:41Z |
| format | Article |
| id | doaj.art-f010a7ed56e940238ef13482da7fbcde |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:16:41Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f010a7ed56e940238ef13482da7fbcde2022-12-21T22:02:05ZengIEEEIEEE Access2169-35362021-01-019674886750010.1109/ACCESS.2021.30751409410543An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble ApproachSapna Singh Kshatri0https://orcid.org/0000-0003-2773-1586Deepak Singh1Bhavana Narain2Surbhi Bhatia3Mohammad Tabrez Quasim4https://orcid.org/0000-0002-5546-0405G. R. Sinha5https://orcid.org/0000-0003-2384-4591Department of Computer Science and Application, MATS University, Raipur, IndiaDepartment of Computer Science and Engineering, Bennett University, Noida, IndiaDepartment of Computer Science and Application, MATS University, Raipur, IndiaDepartment of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al-Hasa, Saudi ArabiaCollege of Computing and Information Technology, University of Bisha, Bisha, Saudi ArabiaDepartment of Electronics & Communication Engineering, Myanmar Institute of Information Technology (MIIT), Mandalay, MyanmarEnsemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naïve byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier.https://ieeexplore.ieee.org/document/9410543/Boosting classifierensemble classifiersIndian crime predictionmachine learningstatistical classifiersstacking |
| spellingShingle | Sapna Singh Kshatri Deepak Singh Bhavana Narain Surbhi Bhatia Mohammad Tabrez Quasim G. R. Sinha An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach IEEE Access Boosting classifier ensemble classifiers Indian crime prediction machine learning statistical classifiers stacking |
| title | An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach |
| title_full | An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach |
| title_fullStr | An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach |
| title_full_unstemmed | An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach |
| title_short | An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach |
| title_sort | empirical analysis of machine learning algorithms for crime prediction using stacked generalization an ensemble approach |
| topic | Boosting classifier ensemble classifiers Indian crime prediction machine learning statistical classifiers stacking |
| url | https://ieeexplore.ieee.org/document/9410543/ |
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