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PatReco: Detection Alexandros Potamianos Dept of ECE, Tech. Univ. of Crete Fall 2009-2010 Detection Classification problems with two classes are sometimes referred to as detection problems For detection problems the two classes are referred to as ω2 and ω1 = NOT ω2 In statistics NOT ω2 is the null hypothesis H0 and ω2 is H1 Detection Goal: Detect an Event Hit (Success): event occurs and is detected False Alarm: event does not occur but is detected Miss (Failure): event occurs and goes undetected Correct Reject: event neither occurs nor detected In traditional Bayes classifier terms: P(correct) = P(Hit) + P(Correct Reject) P(error) = P(False Alarm) + P (Miss) Detection Examples House Alarm (detect burglary) Reading bits of a CD or a DVD (detect 1’s) Medical screening (e.g., detect cancer) Hit (Success): cancer present and detected False Alarm: caner not present but not detected Miss (Failure): cancer present and goes undetected Correct Reject: cancer neither present nor detected Further testing No action Miss Correct Reject Hit False Alarm Receiver Operator Curve (ROC-curve) Equal Error Rate(EER) Operation Point Conclusions Detection is a special case of two-class classification Type I and Type II errors (miss and false alarms) often have different costs Often increase Bayes error to minimize total cost Select an operation point on the ROC-curve