Comparative Analysis of SVM Classifiers in Criminal Profiling Using a Hybridized Algorithm.

The difficult jobs of investigating illegal activity and identifying offenses have fallen to the Law Enforcement Agencies (LEA). To help the LEA solve crimes, a number of criminal profiling systems have been created; however, the methods used in most systems do not allow for the clustering of criminals according to their behavioral traits. In order to get the best SVM classifier with the best kernel function that best fits our crime data set, this study hybridized the Fuzzy C-Means technique with the support vector machine algorithm in criminal profiling. To reduce the intra-cluster variances, the Fuzzy C-Means (FCM) method was altered by hybridizing it with Support Vector Machine (SVM). This was accomplished by substituting the SVM inner-product distance norm for the Euclidean distance used in the current FCM method to calculate the similarity and dissimilarity measure. Next, additional characteristics were added to the data along with the hybridized algorithm. MATLAB scripts were used to implement the developed strategies. The following criteria assessed the performance: execution time, sensitivity, precision, accuracy, and specificity. The result shows that the RBF kernel function performed best with both OAA and OAO classifiers. The OAA classifier performed best with 93.53% Specificity, 96.89% Precision, and 95.44% Accuracy over OAO and the pairwise classifier (BSVM). Therefore, the RBF kernel function using the OAA classifier is recommended to best suit our crime data set for criminal profiling, contributing to Sustainable Development Goal (SDG) 16. [ABSTRACT FROM AUTHOR]