Resource-light method and apparatus for outlier detection

Details Resource-light method and apparatus for outlier detection Resource-light method and apparatus for outlier detection

2007-11-13

2007-11-13

Cottingham, John (Department: 2167)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C706S016000, C706S020000, C706S025000

Reexamination Certificate

active

10749518

ABSTRACT:
Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively. Each iteration consisting of selection of a smaller sub-sample from the input data, training of the underlying classification algorithm with the selected data, and storing the classifier output by the classification algorithm. The selection is done by essentially choosing examples that are harder to classify with the classifiers obtained in the preceding iterations. The final output hypothesis is a voting function of the classifiers obtained in the iterations of the above procedure.

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