Influence in Classification via Cooperative Game Theory (CMU-CyLab-15-001)CyLab
Date of Original Version5-1-2015
Abstract or DescriptionA dataset has been classified by some unknown classifier into two types of points. What were the most important factors in determining the classification outcome? In this work, we employ an axiomatic approach in order to uniquely characterize an influence measure: a function that, given a set of classified points, outputs a value for each feature corresponding to its influence in determining the classification outcome. We show that our influence measure takes on an intuitive form when the unknown classifier is linear. Finally, we employ our influence measure in order to analyze the effects of user profiling on Google’s online display advertising.
Citation InformationAnupam Datta, Amit Datta, Ariel D. Procaccia and Yair Zick. "Influence in Classification via Cooperative Game Theory (CMU-CyLab-15-001)" (2015)
Available at: http://works.bepress.com/yair_zick/2/