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.
Highlights
A lot of research and predictions have been attempted on how to curb crimes by various criminologists and researchers using different modeling and statistical tools
In another side Anifowose, F et al present Artificial Neural Networks ensemble model (ANN) introduces an ensemble model in a different direction that combines various outputs of seven ‘weak’ learning algorithms and compared to the individual ANN, ANN-bagging and Random Forest to create an ensemble solution for the prediction of petroleum reservoir porosity and permeability [17]
The objective of this paper is to examine the evaluation performance on the basis of advanced data mining techniques with 5-fold cross validation technique, with the proposed assemble-stacking based crime prediction method (SBCPM)
Summary
A lot of research and predictions have been attempted on how to curb crimes by various criminologists and researchers using different modeling and statistical tools. Kshatri et al.: Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization with detailed information about major felonies, such as murder, rape, arson etc. The crime predictions are generally suggested by using machine learning techniques with respect to what percentage of future violence is possible in crimes. The most common approaches which have reported achievable accuracy in machine learning classifiers are Random Tree Algorithm, K-Nearest Neighbor(KNN), Bayesian model, Support Vector Machine ( SVM), Neural Network [5]. Among these algorithms, crime prediction technique is proposed by integrating a number of algorithms named as a crime prediction ensemble model using bagging and stacked ensemble techniques, reflecting the beauty of this research work.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
