Patent ID: 7296018

Claim:
A method of outlier detection comprising: generating a plurality of synthesized data, each representing a randomly generated state within a given vector space, said generating including a random number generation; receiving a plurality of real sample data, each representing a detected real event as represented in said given vector space; forming a candidate sample set comprising a union of at least a part of said plurality of synthesized data and said plurality of real sample data, said candidate sample set having a starting population, said candidate sample set being unsupervised as to which members will be classified by said method as being outliers; generating a set of classifiers, each member of said set being a procedure or a representation for a function classifying an operand data as an outlier or a non-outlier, said generating a set of classifiers including; initializing said set of classifiers to be an empty set, selectively sampling said candidate sample data to form a learning data set, said selectively sampling including: i) applying said set of classifiers to each of said candidate sample data and, if any classifiers are extant in said set, generating a corresponding set of classification results, ii) identifying a consistency, for each of said candidate sample data, among said data's corresponding set of classification results, iii) calculating an uncertainty value for each of said candidate sample data based on said identified consistency of said data's corresponding set of classification results, iv) calculating a sampling probability value for each of said candidate sample data based, at least in part, on the corresponding uncertainty value based on said set of classification results, and v) sampling from said candidate data to form said learning data set based, at least in part, on said sampling probability values, such that said learning data set has a population substantially lower than the starting population, generating another classifier based on said learning data set, updating said set of classifiers to include said another classifier, and repeating said selectively sampling, said generating another classifier, and said updating until said set of classifiers includes at least a given minimum t members; and generating an outlier detection algorithm based, at least in part, on at least one of said another classifiers, for classifying a datum as being an outlier or a non-outlier.