US 2024/0256955 A1 · AUTOMATED DATASET REDUCTION BASED ON USE OF EXPLAINABILITY TECHNIQUES

Abstract

Systems, methods, and apparatuses for automatically generating reduced training datasets are described. A training dataset may be inputted into a machine learning model to train the machine learning model to output a label. The machine learning model may comprise nodes, and each of the nodes may be associated with a weight. Based on datapoints, changes to the weight associated with each node of the plurality of nodes may be determined. Using model explainability techniques and based on the changes to the weight associated with each node of the plurality of nodes, pathways that decrease an accuracy of the machine learning model are identified. A first set of the datapoints that correlate with pathways that decrease the accuracy of the machine learning model outputting the label may be determined. Furthermore, the first set of the datapoints may be removed from the training dataset to generate a reduced training dataset.

Claims20

Dependent claims

A method comprising:

inputting, by a computing device, a training dataset into a machine learning model to train the machine learning model to output a label, wherein the machine learning model comprises a plurality of nodes and each node, of the plurality of nodes, is associated with a weight;
determining, based on one or more datapoints of the training dataset, one or more changes to the weight associated with each node of the plurality of nodes;
identifying, using one or more model explainability techniques and based on the one or more changes to the weight associated with each node of the plurality of nodes, one or more pathways that decrease an accuracy of the machine learning model outputting the label;
determining a first set of the one or more datapoints that correlate with the one or more pathways that decrease the accuracy of the machine learning model outputting the label;
and
removing the first set of the one or more datapoints from the training dataset to generate a reduced training dataset.

3 more elements

A non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

inputting a training dataset into a machine learning model to train the machine learning model to output a label, wherein the machine learning model comprises a plurality of nodes and each node, of the plurality of nodes, is associated with a weight;
determining, based on one or more datapoints of the training dataset, one or more changes to the weight associated with each node of the plurality of nodes;
identifying, using one or more model explainability techniques and based on the one or more changes to the weight associated with each node of the plurality of nodes, one or more pathways that decrease an accuracy of the machine learning model outputting the label;
determining a first set of the one or more datapoints that correlate with the one or more pathways that decrease the accuracy of the machine learning model outputting the label;
and
removing the first set of the one or more datapoints from the training dataset to generate a reduced training dataset.

3 more elements

A computing device, comprising:

one or more processors;
and
memory storing instructions that, when executed by the one or more processors, cause the computing device to:
input a training dataset into a machine learning model to train the machine learning model to output a label, wherein the machine learning model comprises a plurality of nodes and each node, of the plurality of nodes, is associated with a weight;
determine, based on one or more datapoints of the training dataset, one or more changes to the weight associated with each node of the plurality of nodes;
identify, using one or more model explainability techniques and based on the one or more changes to the weight associated with each node of the plurality of nodes, one or more pathways that decrease an accuracy of the machine learning model outputting the label;
determine a first set of the one or more datapoints that correlate with the one or more pathways that decrease the accuracy of the machine learning model outputting the label;
and
remove the first set of the one or more datapoints from the training dataset to generate a reduced training dataset.

6 more elements

Dependencies are inferred from the claim text.

Automated dataset reduction based on use of explainability techniques

Life of this patent

Automated dataset reduction based on use of explainability techniques

Life of this patent

Abstract

Claims20