An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach

Abstract

Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naïve byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier.

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

“…In relation to this research, our primary goal is to produce a model that could be useful to the law enforcement unit and to our civilians (More et al, 2021). Our objective is to train our model to accurately classify and forecast the crime category using the test data by using a dynamic ensemble classification algorithm (Keerthi R et al, 2020;Kshatri et al, 2021).This, in turn, could help in planning the deployment of the police force in the area with a high probability of crime occurrences so that it can be prevented prior.…”

“…(Safat et al, 2021) applied various machine learning techniques to predict more than 35 crime types in Chicago and Los Angeles, such as logistic regression, SVM, Naïve Bayes, KNN, decision tree, MLP, random forest, and XGBoost, and time series analysis evaluated with RMSE and MAE by LSTM and ARIMA model to fit the crime data better. (Kshatri et al, 2021) revealed that the assemble-stacking-based crime prediction method (SBCPM) based on the SVM algorithm achieves domain-specific configurations compared with another machine learning model, J48, SMO Naïve byes bagging, and the Random Forest. They also proved that any empirical data on crime is compatible with criminological theories and suggested that the prediction accuracy of the stacking ensemble model is higher than that of the individual.…”

Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

“…Support Vector Machine (SVM) is a supervised learning algorithm that is based on a statistical theory. Support vector machines (SVMs) are a promising machine learning technique that has demonstrated excellent performance in the majority of prediction tasks [67]. The experiment done by [68] was combining SVM with Resnet50 demonstrated the greatest classification rate in experiments.…”

Construction of Blueberry Hardness Prediction Nutrient Content Model Based on Heterogeneous Stacking Ensemble Learning

“…(ADA) and gradient boosting regression tree algorithm (GBRT), extreme gradient boosting algorithm (XGB) based on the idea of gradient boosting, light gradient boosting machine algorithm (LGBM) based on gradient boosting decision tree, and also considered traditional machine learning models such as support vector regression (SVR), arti cial neural networks (Multilayer perceptron was selected in this study, MLP. ), k-nearest neighbors (KNN), etc., and linear regression (LR) models that have been proven to be a way to improve model effectiveness (Kshatri et al, 2021;Rooney et al, 2007).…”