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Feature Engineering for Click Fraud Detection
Transcript of Feature Engineering for Click Fraud Detection
for Click Fraud Detection Clifton, Eng Yeow, Ghim Eng, Kelvin, Minh
Institute for Infocomm Research (I2R) Problem Features Overall Temporal Spatial Potential Methods Results Conclusion Acknow-
ledgments Contact Experience Challenge Meaningful Standing on the
shoulders of giants Rewarding Fraud Publisher Clicks Competition
conditions Data How
many? What? Click behavior Click duplication High-risk
click behavior One minute
periods Countries Publisher's
click feature Discretization Generalized Boosted
Models (GBM) Bernoulli
decision trees 0.001
learning rate depth of 5 5 minimum
observations starrystarrynight is
1st on test set?!? What is the underlying
click fraud scheme? What sort of concealment
strategies commonly used
by fraudulent parties? How to interpret data for
patterns of dishonest
publishers and websites? How to build effective
plans? SMU BuzzCity Worthy
competitors I2R Clifton Phua
firstname.lastname@example.org Questions? Our datasets and details are available at