ONE-CLASS-CLASSIFICATION__一分类问题.ppt

ONE-CLASS CLASSIFICATION Theme presentation for CSI5388 PENGCHENG XI Mar. 09, 2005 papers D.M.J. Tax, One-class classification; Concept-learning in the absence of counter-examples, Ph.D. thesis Delft University of Technology, ASCI Dissertation Series, 65, Delft, 2001, June 19, 1-190. B.Scholkopf, A.J. Smola, and K.R. Muller. Kernel Principal Component Analysis. In B.Scholkopf, C.J.C. Burges, and A.J. Smola, editors, advances in Kernel Methods-SV learning , pp.327-352. MIT Cambridge, MA, 1999. Difference (1) Difference (2) Only information of target class (not outlier class) are available; Boundary between the two classes has to be estimated from data of only genuine class; Task: to define a boundary around the target class (to accept as much of the target objects as possible, to minimizes the chance of accepting outlier objects) Situations Regions in one-class classification (Tradeoff? )Using a uniform outlier distribution also means that when EII is minimized, the data description with minimal volume is obtained. So instead of minimizing both EI and EII, a combination of EI and the volume of the description can be minimized to obtain a good data description. considerations A measure for the distance d(z) or resemblance p(z) of an object z to target class A threshold on this distance or resemblance New objects are accepted: or Error definition A method which obtains the lowest outlier rejection rate, , is to be preferred. For a target acceptance rate , the threshold is defined as: ROC curve with error area (evaluation?) 1-dimensional error measure Varying thresholds along A to B: not on the basis of one single threshold, but integrates their performances over all threshold values Characteristics of one-class approaches Robustness to outliers: * when in a method only the resemblance or distance is optimized, it can therefore be assumed that objects near the threshold are the candidate outlier object