An Improved Support Vector Model with Recursive Feature Elimination for Crime Prediction

Sphamandla I. May, Omowunmi E. Isafiade, Olasupo O. Ajayi

2022

Abstract

The Support Vector Machine (SVM) model has proven relevant in several applications, including crime analysis and prediction. This work utilized the SVM model and developed a predictive model for crime occurrence types. The SVM model was then enhanced using feature selection mechanism, and the enhanced model was compared to the classical SVM. To evaluate the classical and enhanced models, two distinct datasets, one from Chicago and the other from Los Angeles, were used for experiment. In an attempt to enhance the performance of the SVM model and reduce complexity, this work utilised relevant feature selection techniques. We used the Recursive Feature Elimination (RFE) model to enhance SVM’s performance and reduce its complexity, and observed performance increase of an average of 15% from the City of Chicago dataset and 20% from the Los Angeles dataset. Thus, incorporation of appropriate feature selection techniques enhances predictive power of classification algorithms.

Paper Citation

in Harvard Style

May S., Isafiade O. and Ajayi O. (2022). An Improved Support Vector Model with Recursive Feature Elimination for Crime Prediction. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 196-203. DOI: 10.5220/0011524200003335

in Bibtex Style

@conference{kdir22,
author={Sphamandla I. May and Omowunmi E. Isafiade and Olasupo O. Ajayi},
title={An Improved Support Vector Model with Recursive Feature Elimination for Crime Prediction},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={196-203},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011524200003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - An Improved Support Vector Model with Recursive Feature Elimination for Crime Prediction
SN - 978-989-758-614-9
AU - May S.
AU - Isafiade O.
AU - Ajayi O.
PY - 2022
SP - 196
EP - 203
DO - 10.5220/0011524200003335
PB - SciTePress