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ABSTRACT
We provide abstract of feature selection for machine learning in this paper.
Introduction: Crime analysis and prediction have become crucial tasks for law enforcement agencies worldwide. With the advancements in machine learning (ML) techniques, there is a growing interest in utilizing these methodologies to analyze and predict criminal activities. This survey aims to provide an overview of recent research in this field.
Current Approaches in Crime Analysis: Traditional methods of crime analysis often rely on historical data and human expertise. However, they may lack efficiency and accuracy in predicting future criminal incidents. Machine learning offers promising solutions to overcome these limitations by enabling automated analysis of large datasets.
Application of Machine Learning Techniques: Various ML techniques such as classification, clustering, and anomaly detection have been applied to crime analysis tasks. Classification algorithms, including decision trees and support vector machines, are utilized for predicting the type and location of crimes. Clustering methods help in identifying patterns and hotspots of criminal activities.
Challenges and Limitations: Despite the potential benefits, there are several challenges associated with the application of ML in crime analysis. Data quality issues, including incompleteness and bias, can affect the performance of predictive models.
Future Directions: Future research directions in crime analysis using ML techniques include the integration of diverse data sources such as social media and sensor data for more accurate predictions. Additionally, advancements in explainable AI can enhance the interpretability of models, facilitating better decision-making by law enforcement agencies.
Conclusion: In conclusion, machine learning techniques hold significant promise in improving crime analysis and prediction capabilities. By addressing challenges and exploring new research directions, these methodologies can contribute to enhancing public safety and security in communities.
