Patent Document ID: 8006157
Application ID: 11863704
Patent Flag: 1

Claim One:
1. A method for outlier detection, comprising: receiving a plurality of real sample vectors, each real sample vector representing a detected real event; synthesizing a plurality of random state vectors, each random state vector having a randomly generated value; forming a learning set of candidate sample vectors, consisting of a plurality of said random state vectors and a plurality of said synthesized random state vectors; generating a first classifier for classifying said candidate sample vectors between being an outlier or a non-outlier; forming a set of classifiers for classifying candidate sample vectors from among said set of candidate sample vectors between being an outlier or a non-outlier, said forming including initializing said set of classifiers as said first classifier, and adding additional classifiers to said set by repeated iterations, each iteration including generating, for each of said candidate sample vectors, a set of classification results based on said set of classifiers, generating, for each of said candidate sample vectors, a classification uncertainty value, said value reflecting a comparative number, if any, of the classification results indicating the candidate sample as being an outlier to a number, if any, of the classification results indicating the candidate sample as being a non-outlier, updating the learning set of candidate sample vectors by accepting candidate sample vectors for keeping in the updated learning set based on the vector's classification uncertainty value, wherein said accepting is such that a candidate sample vector's probability of being accepted into said updated learning set is proportional to the candidate sample vector's classification uncertainty value, and wherein the population of said updated learning set of candidate sample is substantially lower than the population of the learning set of candidate prior to the updating, generating another classifier based on said learning data set, updating said set of classifiers to include said another classifier, and repeating said iteration until said set of classifiers includes at least t members; generating an outlier detection algorithm based on said set classifiers; and classifying subsequent sample vectors based on said outlier detection algorithm.