Empirical Evaluations of Preprocessing Parameters' Impact on Predictive Coding's Effectiveness

Abstract

Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review. Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by subject-matter experts. Many predictive coding tools require users to rely on static preprocessing parameters and a single machine learning algorithm to develop the predictive model. Little research has been published discussing the impact preprocessing parameters and learning algorithms have on the effectiveness of the technology. A deeper dive into the generation of a predictive model shows that the settings and algorithm can have a strong effect on the accuracy and efficacy of a predictive coding tool. Understanding how these input parameters affect the output will empower legal teams with the information they need to implement predictive coding as efficiently and effectively as possible. This paper outlines different preprocessing parameters and algorithms as applied to multiple real-world data sets to understand the influence of various approaches.

2016 IEEE International Conference on Big Data (Big Data)

Cited by in corpus (6)

• Empirical Study of Deep Learning for Text Classification in Legal Document Review
• Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding
• Empirical Evaluations of Active Learning Strategies in Legal Document Review
• Using Google Analytics to Support Cybersecurity Forensics
• An Empirical Study of the Application of Machine Learning and Keyword Terms Methodologies to Privilege-Document Review Projects in Legal Matters
• Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding