A two-stage classifier that identifies charge and punishment under criminal law of civil law system
Journal article
Authors/Editors
Strategic Research Themes
No matching items found.
Publication Details
Author list: Thammaboosadee S., Watanapa B., Chan J.H., Silparcha U.
Publisher: Institute of Electronics, Information and Communication Engineers
Publication year: 2014
Journal: IEICE Transactions on Information and Systems (0916-8532)
Volume number: E97-D
Issue number: 4
Start page: 864
End page: 875
Number of pages: 12
ISSN: 0916-8532
eISSN: 1745-1361
Languages: English-Great Britain (EN-GB)
View in Web of Science | View on publisher site | View citing articles in Web of Science
Abstract
A two-stage classifier is proposed that identifies criminal charges and a range of punishments given a set of case facts and attributes. Our supervised-learning model focuses only on the offences against life and body section of the criminal law code of Thailand. The first stage identifies a set of diagnostic issues from the case facts using a set of artificial neural networks (ANNs) modularized in hierarchical order. The second stage extracts a set of legal elements from the diagnostic issues by employing a set of C4.5 decision tree classifiers. These linked modular networks of ANNs and decision trees form an effective system in terms of determining power and the ability to trace or infer the relevant legal reasoning behind the determination. Isolated and system-integrated experiments are conducted to measure the performance of the proposed system. The overall accuracy of the integrated system can exceed 90%. An actual case is also demonstrated to show the effectiveness of the proposed system. Copyright ฉ 2014 The Institute of Electronics, Information and Communication Engineers.
Keywords
Criminal law, Data Mining, Decision tree, Legal reasoning
Contact Information