An Improved AdaBoost for Prosecutorial Case-Workload Estimation via Case Grouping - Xin Min; Wei Li; Jinzhao Yang; Weidong Xie; Dazhe Zhao (International Journal of Computational Intelligence Systems, 2022)
Abstract Case-workload estimation has always been a complex process and plays a vital role in prosecutorial work. Despite the increasing development of rule-based techniques, artificial intelligence and machine learning have rarely been used to study case-workload estimation problems, leaving many cases processed without quantitative estimation. This paper aims to develop a new case-work estimation method that combines artificial intelligence methods with practical needs and apply it to the case assignment system of the prosecutor’s office. We propose a feature learning model, the improved AdaBoost model, to capture the features of cases for case grouping to estimate case workload. We first learn the case textual data based on the judicial proper noun dictionary, extract the case labels from the case information with the AdaBoost learner, and group and encode each case by fuzzy matching. Then, the extracted vital information estimates case workload based on the length of case processing time and suspects number, respectively. We conducted extensive experiments to compare the proposed method with eight baseline methods, including the traditional AdaBoost classifier, to evaluate the performance of the proposed model on a real prosecution case dataset. The experimental results demonstrate the superiority of our proposed workload estimation model.