期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2017
卷号：6
期号：2
页码：84-90
出版社：International Journal of Computer and Information Technology
摘要：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.
关键词：Law Enforcement Agencies; crime prediction;
Business Intelligence; WEKA; Performance Analysis;
