Chance discovery and learning minority classes
Chances are viewed in chance discovery as events/situations with significant impact on human decision making. In this research context we are particularly interested in a subset of chances that are unexpected or contradictory with human common knowledge, and the human role that we consider as an essential factor in finding such chances. We first introduce the method LUPC that can learn minority classes from large unbalanced datasets. With its visualization tools as well its exclusive and inclusive constraints, LUPC allows the user to actively participate in and to incorporate background knowledge in the chance discovery process. We then present case studies in which LUPC is used to support the user in discovering significant unexpected chances from stomach cancer and hepatitis databases.
chance discovery learning minority class visualization domain knowledge medical data