CARE Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective We propose a sequential ensemble approach called CARE that employs a two-phase aggregation of the intermediate results. Our ensemble incorporates both the parallel and sequential building blocks to reduce bias as…

Collective Opinion Spam Detection: Bridging Review Networks and Metadata Abstract Online reviews capture the testimonials of “real” people and help shape the decisions of other consumers. Due to the financial gains associated with positive reviews, however, opinion spam has become a widespread problem,…

Less is More: Building Selective Anomaly Ensemble Abstract Ensemble techniques for classification and clustering have long proven effective, yet anomaly ensembles have been barely studied. In this work, we tap into this gap and propose a new ensemble approach for…

Event Detection and Characterization in Dynamic Graphs from shebuti