Patent ID: 7296018
Filing Date: 2007-11-13
Classification: G06N,Y10S

Abstract:
1. 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: 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.