Data Classification Using Ensemble Models

Patent metadata

Field	Value
Publication number	US-2024256637-A1
Application number	US-202318160332-A
Country	US
Kind code	A1
Filing date	Jan 27, 2023
Priority date	Jan 27, 2023
Publication date	Aug 1, 2024
Grant date	—

Abstract

A computer implemented method manages an ensemble model system to classify records. A number of processor units cluster records into groups of records based on classification predictions generated by base models in the ensemble model system for the records. The number of processor units determines sets of weights for the base models that increase a probability that the base models in the ensemble model system correctly predict the groups of records. Each set of weights in the sets of weights is associated with a group of records in the groups of records.

First claim

1 . A computer implemented method, the computer implemented method comprising:
clustering, by a number of processor units, records into groups of records based on classification predictions generated by base models in an ensemble model system for the records; and determining, by the number of processor units, sets of weights for the base models that increase a probability that the base models in the ensemble model system correctly predict the groups of records, wherein each set of weights in the sets of weights is associated with a group of records in the groups of records.

2 . The computer implemented method of claim 1 further comprising:
determining, by the number of processor units, the classification predictions for the records using the base models in the ensemble model system; and
determining, by the number of processor units, thresholds for the base models that meets a set of criteria for the base models in the ensemble model system, wherein each base model in the base models in the ensemble model system has a threshold in the thresholds that meets a set of criteria.

3 . The computer implemented method of claim 1 further comprising:
determining, by the number of processor units, the classification predictions for the records using the base models in the ensemble model system;
determining, by the number of processor units, whether a set of redundant base models is present in the base models in the ensemble model system, wherein a given redundant model in the set of redundant base models has a prediction similarity and model type similarity to another base model of the base models; and
removing, by the number of processor units, the set of redundant base models from the base models in the ensemble model system in response to the set of redundant base models being present.

4 . The computer implemented method of claim 1 , further comprising:
determining, by the number of processor units, the classification predictions for the records using the base models in the ensemble model system; wherein clustering, by the number of processor units, records into groups of records based on the classification predictions generated by base models in the ensemble model system for the records comprises: determining, by the number of processor units, the classification predictions for the records using the base models in the ensemble model system; and placing, by the number of processor units, the records into the groups of records based on similarities between the classification predictions.

5 . The computer implemented method of claim 1 further comprising:
determining, by the number of processor units, the classification predictions for the records using the base models in the ensemble model system; and
selecting, by the number of processor units, a selection policy that uses the classification predictions to classify the records.

6 . The computer implemented method of claim 1 further comprising:
determining, by the number of processor units, the classification predictions for the records using the base models in the ensemble model system;
using, by the number processor units, the base models to determine classification predictions for a new record using the base models in the ensemble model system;
identifying, by the number processor units, a particular group of records in the groups of records most like the new record using based on the classification predictions made by the base models in the ensemble model system;
selecting, by the number processor units, a set of weights in the sets of weights corresponding to the particular group of records; and
classifying the new record using the set of weights in the sets of weights and the classification predictions.

7 . The computer implemented method of claim 6 , wherein classifying the new record using the base models in the ensemble model system using the set of weights in the sets of weights comprises:
applying, by the number processor units, the set of weights to the probabilities for the prediction results to form modified probabilities for the prediction results; and classifying, by the number processor units, the new record using the classification predictions with the modified probabilities for the prediction results.

8 . A computer system comprising:
a number of processor units, wherein the number of processor units executes program instructions to: cluster records into groups of records based on classification predictions generated by base models in the ensemble model system for the records; and determine sets of weights for the base models that increase a probability that the base models in the ensemble model system correctly predict the groups of records, wherein each set of weights in the sets of weights is associated with a group of records in the groups of records.

9 . The computer system of claim 8 , wherein the number of processor units executes the program instructions to:
determine the classification predictions for the records using the base models in the ensemble model system; and determine thresholds for the base models that meets a set of criteria for the base models in the ensemble model system, wherein each base model in the base models in the ensemble model system has a threshold in the thresholds that meets a set of criteria.

10 . The computer system of claim 8 , wherein the number of processor units executes the program instructions to:
determine the classification predictions for the records using the base models in the ensemble model system; determine whether a set of redundant base models is present in the base models in the ensemble model system, wherein a given redundant model in the set of redundant base models has a prediction similarity and model type similarity to another base model of the base models; and remove the set of redundant base models from the base models in the ensemble model system in response to the set of redundant base models being present.

11 . The computer system of claim 8 , further comprising:
determine the classification predictions for the records using the base models in the ensemble model system; wherein in clustering records into groups of records based on the classification predictions generated by base models in the ensemble model system for the records, the number of processor units executes the program instructions to: determine the classification predictions for the records using the base models in the ensemble model system; and place the records into the groups of records based on similarities between the classification predictions.

12 . The computer system of claim 8 , wherein the number of processor units executes the program instructions to:
determine the classification predictions for the records using the base models in the ensemble model system; and select a selection policy that uses the classification predictions to classify the records.

13 . The computer system of claim 8 , wherein the number of processor units executes the program instructions to:
determine the classification predictions for the records using the base models in the ensemble model system; use the base models to determine a classification prediction for a new record using the base models in the ensemble model system; identify a particular group of records in the groups of records most like the new record based on the classification predictions made by the base models in the ensemble model system; select a set of weights in the sets of weights corresponding to the particular group of records; and classify the new record using the set of weights in the sets of weights and the classification predictions.

Assignees

• IBM

Inventors

• Han Si Er
• Zhang xue ying
• Xu Jing
• Xu Jing James
• Ma Xiao Ming
• Yu wen pei
• Wang Jun
• Yang ji hui

Classifications

• G06F16/285

  Clustering or classification · CPC title

• G06F18/2321 Primary

  using statistics or function optimisation, e.g. modelling of probability density functions · CPC title

• G06F18/241 Primary

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