A study on classification learning algorithms to predict crime status.

Citation

Shojaee, Somayeh and Mustapha, Aida and Sidi, Fatimah and A. Jabar, Marzanah (2013) A study on classification learning algorithms to predict crime status. International Journal of Digital Content Technology and its Applications, 7 (9). pp. 361-369. ISSN 1975-9339

Abstract

In the recent past, there has been a huge increase in the crime rate, hence the significance of task to predict, prevent or solve the crimes. In this paper, we conducted an experiment to obtain better supervised classification learning algorithms to predict crime status by using two different feature selection methods tested on real dataset. Comparisons in terms of Area Under Curve (AUC), that Naïve Bayesian (0.898), k-Nearest Neighbor (k-NN) (0.895) and Neural Networks (MultilayerPerceptron) (0.892) are better classifiers against Decision Tree (J48) (0.727), and Support Vector Machine (SVM) (0.678). Furthermore, the performance of mining results is improved by using Chi-square feature selection technique.

Download File

Preview

PDF (Abstract)
A study on classification learning algorithms to predict crime status.pdf
Download (83kB) | Preview

Additional Metadata

Item Type:	Article
Divisions:	Faculty of Computer Science and Information Technology
Publisher:	Advanced Institute of Convergence Information Technology
Keywords:	Classification; AUC; Naive bayesian; Neural networks; k-nearest neighbor; Decision tree; Support vector machine; Chi-square.
Depositing User:	Ms. Nida Hidayati Ghazali
Date Deposited:	14 Jul 2014 06:07
Last Modified:	28 Jan 2016 07:45
URI:	http://psasir.upm.edu.my/id/eprint/30681
Statistic Details:	View Download Statistic

Actions (login required)

View Item