Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate.
Crime forecasting is beneficial as it provides valuable information to the government and authorities in planning an efficient crime prevention measure. Most criminology studies found that influence from several factors, such as social, demographic, and economic factors, significantly affects crime...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2023
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| Online Access: | http://eprints.utm.my/106756/1/AlifRidzuanKhairuddin2023_HybridNeighbourhoodComponentAnalysisWithGradientTree.pdf |
| _version_ | 1811132405891727360 |
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| author | Khairuddin, Alif Ridzuan Alwee, Razana Haron, Habibollah |
| author_facet | Khairuddin, Alif Ridzuan Alwee, Razana Haron, Habibollah |
| author_sort | Khairuddin, Alif Ridzuan |
| collection | ePrints |
| description | Crime forecasting is beneficial as it provides valuable information to the government and authorities in planning an efficient crime prevention measure. Most criminology studies found that influence from several factors, such as social, demographic, and economic factors, significantly affects crime occurrence. Therefore, most criminology experts and researchers study and observe the effect of factors on criminal activities as it provides relevant insight into possible future crime trends. Based on the literature review, the applications of proper analysis in identifying significant factors that influence crime are scarce and limited. Therefore, this study proposed a hybrid model that integrates Neighbourhood Component Analysis (NCA) with Gradient Tree Boosting (GTB) in modelling the United States (US) crime rate data. NCA is a feature selection technique used in this study to identify the significant factors influencing crime rate. Once the significant factors were identified, an artificial intelligence technique, i.e., GTB, was implemented in modelling the crime data, where the crime rate value was predicted. The performance of the proposed model was compared with other existing models using quantitative measurement error analysis. Based on the result, the proposed NCA-GTB model outperformed other crime models in predicting the crime rate. As proven by the experimental result, the proposed model produced the smallest quantitative measurement error in the case study. |
| first_indexed | 2024-09-24T00:03:32Z |
| format | Article |
| id | utm.eprints-106756 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-09-24T00:03:32Z |
| publishDate | 2023 |
| publisher | Universiti Utara Malaysia Press |
| record_format | dspace |
| spelling | utm.eprints-1067562024-07-17T07:19:57Z http://eprints.utm.my/106756/ Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. Khairuddin, Alif Ridzuan Alwee, Razana Haron, Habibollah T58.5-58.64 Information technology Crime forecasting is beneficial as it provides valuable information to the government and authorities in planning an efficient crime prevention measure. Most criminology studies found that influence from several factors, such as social, demographic, and economic factors, significantly affects crime occurrence. Therefore, most criminology experts and researchers study and observe the effect of factors on criminal activities as it provides relevant insight into possible future crime trends. Based on the literature review, the applications of proper analysis in identifying significant factors that influence crime are scarce and limited. Therefore, this study proposed a hybrid model that integrates Neighbourhood Component Analysis (NCA) with Gradient Tree Boosting (GTB) in modelling the United States (US) crime rate data. NCA is a feature selection technique used in this study to identify the significant factors influencing crime rate. Once the significant factors were identified, an artificial intelligence technique, i.e., GTB, was implemented in modelling the crime data, where the crime rate value was predicted. The performance of the proposed model was compared with other existing models using quantitative measurement error analysis. Based on the result, the proposed NCA-GTB model outperformed other crime models in predicting the crime rate. As proven by the experimental result, the proposed model produced the smallest quantitative measurement error in the case study. Universiti Utara Malaysia Press 2023-04 Article PeerReviewed application/pdf en http://eprints.utm.my/106756/1/AlifRidzuanKhairuddin2023_HybridNeighbourhoodComponentAnalysisWithGradientTree.pdf Khairuddin, Alif Ridzuan and Alwee, Razana and Haron, Habibollah (2023) Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. Journal of Information and Communication Technology, 22 (2). pp. 207-229. ISSN 1675-414X http://dx.doi.org/10.32890/jict2023.22.2.3 DOI:10.32890/jict2023.22.2.3 |
| spellingShingle | T58.5-58.64 Information technology Khairuddin, Alif Ridzuan Alwee, Razana Haron, Habibollah Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. |
| title | Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. |
| title_full | Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. |
| title_fullStr | Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. |
| title_full_unstemmed | Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. |
| title_short | Hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate. |
| title_sort | hybrid neighbourhood component analysis with gradient tree boosting for feature selection in forecasting crime rate |
| topic | T58.5-58.64 Information technology |
| url | http://eprints.utm.my/106756/1/AlifRidzuanKhairuddin2023_HybridNeighbourhoodComponentAnalysisWithGradientTree.pdf |
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