FC-NT: a crime prediction algorithm incorporating formal context and neural networks

Feifei Zao; Lingling Lv; Feng Ding; Shixian Jin; Xiaoding Guo

doi:10.1117/12.3006077

10 October 2023 FC-NT: a crime prediction algorithm incorporating formal context and neural networks

Feifei Zao, Lingling Lv, Feng Ding, Shixian Jin, Xiaoding Guo

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279903 (2023) https://doi.org/10.1117/12.3006077
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Feifei Zao, Lingling Lv, Feng Ding, Shixian Jin, and Xiaoding Guo "FC-NT: a crime prediction algorithm incorporating formal context and neural networks", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279903 (10 October 2023); https://doi.org/10.1117/12.3006077

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available