Post-processing output data of a classifier

Patent metadata

Field	Value
Publication number	US-2021397900-A1
Application number	US-202117347645-A
Country	US
Kind code	A1
Filing date	Jun 15, 2021
Priority date	Jun 19, 2020
Publication date	Dec 23, 2021
Grant date	—

Abstract

Provided is a computer-implemented method for post-processing output data of a classifier, including the steps: a. providing a validation data set with a plurality of labelled sample pairs, wherein each labelled sample pair comprises a model input and a corresponding model output; b. providing a plurality of perturbation levels; c. generating at least one perturbated sample pair for each labelled sample pair of the plurality of labelled sample pairs using a perturbation method based on the respective labelled sample pair and at least one perturbation level of the plurality of perturbation levels; d. determining a post-processing model based on the plurality of perturbated sample pairs; e. applying the determined post-processing model on testing data to post-process the output data of the classifier; and f. providing the post-processed output data of the classifier. Also provided is a corresponding technical unit and computer program product.

First claim

1 . A computer-implemented method for post-processing output data of a classifier, comprising:
a. providing a validation data set with a plurality of labelled sample pairs, wherein each labelled sample pair comprises a model input and a corresponding model output; b. providing a plurality of perturbation levels; c. generating at least one perturbated sample pair for each labelled sample pair of the plurality of labelled sample pairs using a perturbation method based on the respective labelled sample pair and at least one perturbation level of the plurality of perturbation levels; d. determining a post-processing model based on the plurality of perturbated sample pairs; e. applying the determined post-processing model on testing data to post-process the output data of the classifier; and f. providing the post-processed output data of the classifier.

2 . The computer-implemented method according to claim 1 , wherein the classifier is a trained machine learning model selected from the group comprising: SVM, xgboost, random forest and neural network.

3 . The computer-implemented method according to claim 1 , wherein the perturbation method is a noise function selected from the group comprising: Fast gradient sign method (FGSM) and Gaussian function.

4 . A technical unit for performing the method steps according to claim 1 .

5 . A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method directly loadable into an internal memory of a computer, comprising software code portions for performing the steps according to claim 1 when the computer program product is running on a computer.

Assignees

• Siemens Ag

Inventors

• Büttner Florian
• Gruber Sebastian

Classifications

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F18/285

  Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system · CPC title

• G06F18/241 Primary

  relating to the classification model, e.g. parametric or non-parametric approaches · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title