Authors:
Sphamandla I. May
;
Omowunmi E. Isafiade
and
Olasupo O. Ajayi
Affiliation:
Department of Computer Science, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
Keyword(s):
Crime Prediction, Support Vector Machine, Recursive Feature Elimination, Feature Selection.
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.