A framework of multi-stage classifier for identifying criminal law sentences
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Publication Details
Author list: Thammaboosadee S., Watanapa B., Charoenkitkarn N.
Publisher: Elsevier
Publication year: 2012
Volume number: 13
Start page: 53
End page: 59
Number of pages: 7
ISSN: 1877-0509
Languages: English-Great Britain (EN-GB)
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Abstract
This paper proposes a framework to identify the relevant law articles consisting of sentences and range of punishments, given facts discovered in the criminal case of interest. The model is formulated as a two-stage classifier according to the concept of machine learning. The first stage is to determine a set of case diagnostic issues, using a modular Artificial Neural Network (mANN), and the second stage is to determine the relevant legal elements which lead to legal charges identification, using SVM-equipped C4.5. The integrated multi-stage model aims at achieving high accuracy of classification while reserving "arguability". Hypothetically, mANN handles well for digesting complexity in case-level issues analysis with acceptable explanatory power and C4.5 addresses the lesser extent of contingency and provides humaninterpretable logic concerning the high-level context of legal codes. ฉ 2012 Published by Elsevier B.V.
Keywords
Criminal law, Data Mining, Decision tree, Legal reasoning
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