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Lawsuit lead time prediction: Comparison of data mining techniques based on categorical response variable

机译：诉讼提前期预测：基于分类响应变量的数据挖掘技术比较

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摘要

The quality of the judicial system of a country can be verified by the overall length time of lawsuits, or the lead time. When the lead time is excessive, a country’s economy can be affected, leading to the adoption of measures such as the creation of the Saturn Center in Europe. Although there are performance indicators to measure the lead time of lawsuits, the analysis and the fit of prediction models are still underdeveloped themes in the literature. To contribute to this subject, this article compares different prediction models according to their accuracy, sensitivity, specificity, precision, and F1 measure. The database used was from TRF4—the Tribunal Regional Federal da 4a Região—a federal court in southern Brazil, corresponding to the 2nd Instance civil lawsuits completed in 2016. The models were fitted using support vector machine, naive Bayes, random forests, and neural network approaches with categorical predictor variables. The lead time of the 2nd Instance judgment was selected as the response variable measured in days and categorized in bands. The comparison among the models showed that the support vector machine and random forest approaches produced measurements that were superior to those of the other models. The evaluation of the models was made using k-fold cross-validation similar to that applied to the test models.
机译：一个国家司法系统的质量可以通过诉讼的总时长或准备时间来验证。如果交货时间过长，则可能会影响一个国家的经济，从而导致采取诸如在欧洲建立土星中心之类的措施。尽管有性能指标可衡量诉讼的准备时间，但文献中尚未充分分析和预测模型的适用性。为了对此主题做出贡献，本文根据其准确性，敏感性，特异性，准确性和F1度量比较了不同的预测模型。使用的数据库来自TRF4，即巴西南部的联邦地方法院区域法庭da 4，在2016年完成的第二次民事诉讼对应。使用支持向量机，朴素贝叶斯，随机森林和带有分类预测变量的神经网络方法拟合模型。选择第2个sup实例判断的前置时间作为以天为单位测量的响应变量，并按波段进行分类。模型之间的比较表明，支持向量机和随机森林方法产生的测量结果优于其他模型。使用与测试模型相似的k倍交叉验证对模型进行评估。

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