NDLI: Improved method of classification algorithms for crime prediction

Content Provider	IEEE Xplore Digital Library
Author	Babakura, A. Sulaiman, M.N. Yusuf, M.A.
Copyright Year	2014
Description	Author affiliation: Fac. of Comput. Sci. & Inf. Technol., Univ. Putra Malaysia (UPM), Serdang, Malaysia (Babakura, A.; Sulaiman, M.N.; Yusuf, M.A.)
Abstract	The growing availability of information technologies has enabled law enforcement agencies to collect detailed data about various crimes. Classification is the procedure of finding a model (or function) that depicts and distinguishes data classes or notions, with the end goal of having the ability to utilize the model to predict the crime labels. In this research classification is applied to crime dataset to predict the “crime category” for diverse states of the United States of America (USA). The crime data set utilized within this research is real in nature, it was gathered from socio-economic data from 1990 US census. Law enforcement data from 1990 US LEMAS survey, and from the 1995 FBI UCR. This paper compares two different classification algorithms namely - Naïve Bayesian and Back Propagation (BP) for predicting “Crime Category” for distinctive states in USA. The result from the analysis demonstrated that Naïve Bayesian calculation out performed BP calculation and attained the accuracy of 90.2207% for group 1 and 94.0822% for group 2. This clearly indicates that Naïve Bayesian calculation is supportive for prediction in diverse states in USA.
Starting Page	250
Ending Page	255
File Size	286606
Page Count	6
File Format	PDF
e-ISBN	9781479964444
DOI	10.1109/ISBAST.2014.7013130
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Feature selection Algorithms Pre-processing Predictive models Prediction algorithms Data models Crime Category Crime prediction Classification algorithms Bayes methods Data mining
Content Type	Text
Resource Type	Article

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