A Comparative Study to Evaluate Filtering Methods for Crime Data Feature Selection

Abstract

In this study, we present a comparative study on correlation and information gain algorithms to evaluate and produce the subset of crime features. The main objective of the study is to find a subset of attributes from a dataset described by a feature set and to classify the crimes into three different categories; low, medium and high. The experiment is carried out on the communities and crime dataset using WEKA, an open source data mining software. Based on attributes chosen by five features selection methods, the accuracy rates of several classification algorithms were obtained for analysis. The results from the experiment demonstrated that, the correlation method out performed information gain and human expert with a mean accuracy of 96.94% for entire classifier and FSs with 13 optimal features selection. This subset feature is important information for classification and can be effectively applied to crime dataset to predict crime category for different state and directly support decision making in crime prevention system.