ALLIE: Active Learning on Large-scale Imbalanced Graphs
Limeng Cui, Xianfeng Tang, Sumeet Katariya, Nikhil Rao, Pallav Agrawal, Karthik Subbian, Dongwon Lee
Abstract
Human labeling is time-consuming and costly. This problem is further exacerbated in extremely imbalanced class label scenarios, such as detecting fraudsters in online websites. Active learning selects the most relevant example for human labelers to improve the model performance at a lower cost. However, existing methods for active learning for graph data often assumes that both data and label distributions are balanced. These assumptions fail in extreme rare-class classification scenarios, such as classifying abusive reviews in an e-commerce website.
Topics & Concepts
Computer scienceMachine learningArtificial intelligenceClass (philosophy)Active learning (machine learning)GraphTheoretical computer scienceImbalanced Data Classification TechniquesSpam and Phishing DetectionText and Document Classification Technologies