Machine-learning techniques for class imbalances and adversaries
Many areas of applied machine learning require models optimized for rare occurrences, such as class imbalances, and users actively attempting to subvert the system (adversaries). Brendan Herger offers an overview of multiple published techniques that specifically attempt to address these issues and discusses lessons learned by the Data Innovation Lab at Capital One.
| Talk Title | Machine-learning techniques for class imbalances and adversaries |
| Speakers | |
| Conference | Strata + Hadoop World |
| Conf Tag | Make Data Work |
| Location | New York, New York |
| Date | September 27-29, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Many areas of applied machine learning require models optimized for rare occurrences, such as class imbalances, and users actively attempting to subvert the system (adversaries). The Data Innovation Lab at Capital One has explored advanced modeling techniques for just these challenges. The lab’s use case necessitated that it survey the many related fields that deal with these issues and perform many of the suggested modeling techniques. It has also introduced a few novel variations of its own. Brendan Herger offers an introduction to the problem space and a brief overview of the modeling frameworks the Data Innovation Lab has chosen to work with, outlines the lab’s approaches, discusses the lessons learned along the way, and explores proposed future work. Topics include:
