Improvement of Criminal Identification by Smart Optimization Method

Reem Razzaq Abdul Hussein,Dr Salih Mahdi Al-Qaraawi,Dr.Muayad Sadik Croock,F Hage Chehade

doi:10.1051/matecconf/201928105003

Reem Razzaq Abdul Hussein, Dr Salih Mahdi Al-Qaraawi + Show 2 more

Open Access

https://doi.org/10.1051/matecconf/201928105003

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Abstract

Data-mining methods, which can be optimized via different methods, are applied in crime detection. This work, the decision tree algorithm is used for classifying and optimizing its structure with the smart method. This method is applied to two datasets: Iraq and India criminals. The goal of the proposed method is to identify criminals using a mining method based on smart search. This contribution helps in the acquisition of better results than those provided by traditional mining methods via controlling the size of the tree through decreasing leaf size.

Highlights

For data mining, classification techniques are widely used in e-government, especially in the criminology field [1]
A machine learning (ML) model may require different constraints, model selection, and learning rates to make generalizations for various data patterns. These measures are called hyperparameters, which must be tuned for the model to solve ML optimally [3]; previous researchers have used hyperparameters in different strategies; several researchers have used grid search, which is commonly known as a brute force or exhaustive search; problems, such as high dimensionality and parallelization, occur because the hyperparameter settings it generates evaluates independently for each other [4]; other researchers have used random search, rather than brute force search; in our work, we depend on Bayesian optimization (BO) as a sequential model-based optimization algorithm, which depends on the outcomes of the previous iteration to improve the sampling method for the subsequent experiment. others used BO for hyperparameters to generalize the Gaussian procedure
The main contribution is using the smart search method combined with DT to obtain an accurate model, in which the leaf size is decreased and the pruning level can be improved to reduce the crossvalidation loss of DT

Summary

Introduction

Classification techniques are widely used in e-government, especially in the criminology field [1] They help police departments to predicate criminals and information about crime locations. A machine learning (ML) model may require different constraints, model selection, and learning rates to make generalizations for various data patterns These measures are called hyperparameters, which must be tuned for the model to solve ML optimally [3]; previous researchers have used hyperparameters in different strategies; several researchers have used grid search, which is commonly known as a brute force or exhaustive search; problems, such as high dimensionality and parallelization, occur because the hyperparameter settings it generates evaluates independently for each other [4]; other researchers have used random search, rather than brute force search; in our work, we depend on Bayesian optimization (BO) as a sequential model-based optimization algorithm, which depends on the outcomes of the previous iteration to improve the sampling method for the subsequent experiment. The proposed method is offering help to employees of Iraq National Identifiers in criminal detection

DT Algorithms with Hyperparameter Optimization

Result and Implementation

Findings

Conclusion