Criminal Conviction Classification Based on Multiple Learning Methods

Xi Yang, Xudong Luo, Ying Liu
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Abstract

The application of artificial intelligence in the legal field can save a lot of the time for legal professionals. In particular, in this paper we propose a method for predicting what kind of conviction a suspect has according to the facts of the crime of the suspect. Specifically, we first pre-process the data and then use multiple classification methods to classify the crime facts, and finally combine the results of each model to gain a more accurate of conviction classification.
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基于多元学习方法的刑事定罪分类
人工智能在法律领域的应用可以为法律专业人员节省大量的时间。本文特别提出了一种根据犯罪嫌疑人的犯罪事实来预测犯罪嫌疑人有罪程度的方法。具体而言,我们首先对数据进行预处理,然后使用多种分类方法对犯罪事实进行分类,最后将各个模型的结果结合起来,以获得更准确的定罪分类。
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