A Performance Analysis of Business Intelligence Techniques on Crime Prediction
Law Enforcement agencies are faced with a problem of effectively predicting the likelihood of crime happening given the past crime data which would otherwise help them to do so. There is a need to identify the most efficient algorithm that can be used in crime prediction given the past crime data. I...
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International Journal of Computer and Information Technology
2018
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| Online Access: | http://hdl.handle.net/20.500.12493/112 |
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| author | Ivan, Niyonzima Emmanuel Ahishakiye Elisha Opiyo Omulo Ruth Wario |
| author_facet | Ivan, Niyonzima Emmanuel Ahishakiye Elisha Opiyo Omulo Ruth Wario |
| author_sort | Ivan, Niyonzima |
| collection | KAB-DR |
| description | Law Enforcement agencies are faced with a problem of effectively predicting the likelihood of crime happening given the past crime data which would otherwise help them to do so. There is a need to identify the most efficient algorithm that can be used in crime prediction given the past crime data. In this research, Business intelligence techniques considered was based on supervised learning (Classification) techniques given that labeled training data was available. Four different classification algorithms that is; decision tree (J48), Naïve Bayes, Multilayer Perceptron and Support Vector Machine were compared to find the most effective algorithm for crime prediction. The study used classification models generated using Waikato Environment for Knowledge Analysis (WEKA). Manual method of attribute selection was used; this is because it works well when there is large number of attributes. The dataset was acquired from UCI machine learning repository website with a title ‘Crime and Communities’. The data set had 128 attributes of which 13 were selected for the study. The study revealed that the accuracy of J48, Naïve bayes, Multilayer perceptron and Support Vector Machine (SMO) is approximately 100%, 89.7989%, 100% and 92.6724%, respectively for both training and test data. Also the execution time in seconds of J48, Naïve bayes, Multilayer perceptron and SVO is 0.06, 0.14, 9.26 and 0.66 respectively using windows7 32 bit. Hence, Decision Tree (J48) out performed Naïve bayes, Multilayer perceptron and Support Vector Machine (SMO) algorithms, and manifested higher performance both in execution time and in accuracy. The scope of this project was to identify the most effective and accurate Business intelligence technique that can be used during crime data mining to provide accurate results. |
| format | Article |
| id | oai:idr.kab.ac.ug:20.500.12493-112 |
| institution | KAB-DR |
| publishDate | 2018 |
| publisher | International Journal of Computer and Information Technology |
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| spelling | oai:idr.kab.ac.ug:20.500.12493-1122024-01-17T04:45:48Z A Performance Analysis of Business Intelligence Techniques on Crime Prediction Ivan, Niyonzima Emmanuel Ahishakiye Elisha Opiyo Omulo Ruth Wario Law Enforcement Agencies; crime prediction; Business Intelligence; WEKA; Performance Analysis Law Enforcement agencies are faced with a problem of effectively predicting the likelihood of crime happening given the past crime data which would otherwise help them to do so. There is a need to identify the most efficient algorithm that can be used in crime prediction given the past crime data. In this research, Business intelligence techniques considered was based on supervised learning (Classification) techniques given that labeled training data was available. Four different classification algorithms that is; decision tree (J48), Naïve Bayes, Multilayer Perceptron and Support Vector Machine were compared to find the most effective algorithm for crime prediction. The study used classification models generated using Waikato Environment for Knowledge Analysis (WEKA). Manual method of attribute selection was used; this is because it works well when there is large number of attributes. The dataset was acquired from UCI machine learning repository website with a title ‘Crime and Communities’. The data set had 128 attributes of which 13 were selected for the study. The study revealed that the accuracy of J48, Naïve bayes, Multilayer perceptron and Support Vector Machine (SMO) is approximately 100%, 89.7989%, 100% and 92.6724%, respectively for both training and test data. Also the execution time in seconds of J48, Naïve bayes, Multilayer perceptron and SVO is 0.06, 0.14, 9.26 and 0.66 respectively using windows7 32 bit. Hence, Decision Tree (J48) out performed Naïve bayes, Multilayer perceptron and Support Vector Machine (SMO) algorithms, and manifested higher performance both in execution time and in accuracy. The scope of this project was to identify the most effective and accurate Business intelligence technique that can be used during crime data mining to provide accurate results. Kabale University 2018-11-01T09:12:22Z 2018-11-01T09:12:22Z 2017 Article Niyonzima, I. A Performance Analysis of Business Intelligence Techniques on Crime Prediction. (2017)Journal of Computer and Information Technology (ISSN: 2279 – 0764) Volume 06– Issue 02, March 2017 http://hdl.handle.net/20.500.12493/112 application/pdf International Journal of Computer and Information Technology |
| spellingShingle | Law Enforcement Agencies; crime prediction; Business Intelligence; WEKA; Performance Analysis Ivan, Niyonzima Emmanuel Ahishakiye Elisha Opiyo Omulo Ruth Wario A Performance Analysis of Business Intelligence Techniques on Crime Prediction |
| title | A Performance Analysis of Business Intelligence Techniques on Crime Prediction |
| title_full | A Performance Analysis of Business Intelligence Techniques on Crime Prediction |
| title_fullStr | A Performance Analysis of Business Intelligence Techniques on Crime Prediction |
| title_full_unstemmed | A Performance Analysis of Business Intelligence Techniques on Crime Prediction |
| title_short | A Performance Analysis of Business Intelligence Techniques on Crime Prediction |
| title_sort | performance analysis of business intelligence techniques on crime prediction |
| topic | Law Enforcement Agencies; crime prediction; Business Intelligence; WEKA; Performance Analysis |
| url | http://hdl.handle.net/20.500.12493/112 |
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