Cost-Sensitive Algorithms for Text Classification in the Legal Domain:

Addressing Imbalanced Lawsuit Themes

Abstract

This article discusses the challenges of imbalanced classification in machine learning, where algorithms often incorrectly assume an even distribution of instances between classes. This issue is common in real-world scenarios, leading to poor representation of minority classes in training data. To combat this, Cost-Sensitive Learning techniques have been developed, focussing on minimising the overall cost of misclassification rather than merely optimising accuracy. These techniques are categorised into three types: Cost-Sensitive Resampling, Algorithms, and Hybrid techniques. The research presents a case study on classifying lawsuits into repetitive themes in São Paulo Court, Brazil, using these cost-sensitive approaches on an imbalanced dataset. The goal is to automate the classification of lawsuits to save time, use human resources more effectively, and speed up the resolution of the lawsuit. The study highlights the effectiveness of cost-sensitive techniques in handling imbalanced classification and their benefits in real-world applications, particularly in the legal field, by improving efficiency and reducing manual workload and processing time for lawsuits.