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Influence in Classification via Cooperative Game Theory (CMU-CyLab-15-001)
CyLab
  • Anupam Datta, Carnegie Mellon University
  • Amit Datta, Carnegie Mellon University
  • Ariel D. Procaccia, Carnegie Mellon University
  • Yair Zick, Carnegie Mellon University
Date of Original Version
5-1-2015
Type
Technical Report
Abstract or Description

A 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 Information
Anupam 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/