Method and device for predicting abnormal sample

A technology of abnormal samples and samples, applied in prediction, kernel method, reasoning method, etc., can solve the problems of data that are difficult to detect, small in quantity, and difficult to be found, and achieve the effect of avoiding information loss, avoiding computing obstacles, and making accurate predictions.

A technology of abnormal samples and samples, applied in prediction, kernel method, reasoning method, etc., can solve the problems of data that are difficult to detect, small in quantity, and difficult to be found, and achieve the effect of avoiding information loss, avoiding computing obstacles, and making accurate predictions.

CN108595495AActive Publication Date: 2018-09-28ADVANCED NEW TECH CO LTD
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  • Method and device for predicting abnormal sample
  • Method and device for predicting abnormal sample
  • Method and device for predicting abnormal sample

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Embodiment Construction

[0037] The following describes the solutions provided in this specification with reference to the drawings.

[0038] figure 1 This is a schematic diagram of an implementation scenario of an embodiment disclosed in this specification. Such as figure 1 As shown, the computing platform 100, such as an Alipay server, trains a prediction model based on a normal historical sample set (such as a normal historical transaction sample set) by using a support vector domain description SVDD method. In the training process, in order to avoid calculation difficulties caused by too high sample dimensions, for each historical sample, the computing platform 100 uses multiple dimensionality reduction methods to reduce the dimensionality of each historical sample to obtain multiple dimensionality reduction sample sets, and then use the SVDD method respectively. By learning the dimensionality reduction sample set, multiple processing models are obtained. These processing models can be considered as ...

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Abstract

The embodiment of the specification provides a method and a device for predicting an abnormal sample. The method comprises the following steps that: firstly, obtaining a sample to be predicted, then,adopting a plurality of dimensionality reduction methods to independently carry out dimensionality reduction processing on the sample to be predicted to obtain a plurality of processed samples; then,independently inputting the plurality of processing samples into a plurality of processing models to obtain the score of each processing sample, wherein the ith processing model Mi scores a corresponding processing sample on the basis of a hypersphere determined by a SVDD (Support Vector Data Description) way in a corresponding dimensionality space in advance to score the corresponding processingsample; then, according to the score of each processing sample, determining the comprehensive score of the sample to be predicted; and finally, according to the comprehensive score, determining whether the sample to be predicted is an abnormal sample or not. Therefore, whether an unknown sample is an abnormal sample or not can be more effectively predicted.

Description

Technical field [0001] One or more embodiments of this specification relate to the field of sample classification using a computer, and in particular to a method and device for predicting abnormal samples. Background technique [0002] With the development of computer and Internet technology, a large number of data and samples have been produced. In many scenarios, it is necessary to classify these data and samples, such as distinguishing whether it is a normal sample or an abnormal sample. For example, in payment and transaction services, it is often necessary to distinguish between normal transaction samples and abnormal transaction samples (for example, cash out, financial fraud transactions, etc.), so as to better prevent payment risks. In the field of security access, it is often necessary to distinguish between normal access data and abnormal access data. The abnormal access data often comes from some users trying to invade or obtain illegal data through illegal access. S...

Claims

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Application Information

Patent Timeline
28 Sep 2018
Publication
CN108595495A
IPC
G06F17/30; G06Q10/04
CPC
G06Q10/04; G06Q40/00; G06N20/10; G06N20/20; G06N7/01; G06F16/285; G06F17/16; G06F17/18
Inventors
张雅淋; 李龙飞