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公开(公告)号：US20220366043A1
公开(公告)日：2022-11-17
申请号：US17238248
申请日：2021-04-23
Applicant: Booz Allen Hamilton Inc.
Inventor： Robert J. JOYCE , Edward RAFF
Abstract: Disclosed are methods and apparatuses for classifier evaluation. The evaluation involves constructing a ground truth refinement having a degree of error within specified bounds from a malware reference dataset as an approximate ground truth refinement. The evaluation further involves using the approximate ground truth refinement to determine at least one of: a lower bound on precision or an upper bound on recall and accuracy. The evaluation further involves evaluating a classifier by evaluating at least one of a classification method or clustering method by examining changes to the upper bound and/or the lower bound produced by the approximate ground truth refinement.
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公开(公告)号：US20200286001A1
公开(公告)日：2020-09-10
申请号：US16536926
申请日：2019-08-09
Applicant: Booz Allen Hamilton Inc.
Inventor： Andre Tai NGUYEN , Edward RAFF
Abstract: A computer-implemented method for generating an interpretable kernel embedding for heterogeneous data. The method can include identifying a set of base kernels in the heterogeneous data; and creating multiple sets of transformed kernels by applying a unique composition rule or a unique combination of multiple composition rules to the set of base kernels. The method can include fitting the multiple sets into a stochastic process model to generate fitting scores that respectively indicate a degree of the fitting for each of the multiple sets; storing the fitting scores in a matrix; and standardizing the matrix to generate the interpretable kernel embedding for the heterogeneous data.
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