# US 11,443,244 B2 · Parallel ensemble of machine learning algorithms

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- Number: US 11,443,244 B2
- Document ID: US11443244B2
- Status: Granted
- Kind: B2
- Application number: 16/431,802
- Filed: 2019-06-05
- Published: 2020-12-10
- Granted: 2022-09-13
- Earliest claimed: 2019-06-05
- Assignees: International Business Machines Corportation
- Applicants: International Business Machines Corporation
- Inventors: Charles E. Hackett
- Web page: https://patentgrep.com/patent/US11443244B2

## CPC

- G06N 20/20: Ensemble learning
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

An aspect of the invention includes receiving machine learning (ML) training data that includes a plurality of features for a plurality of observations. The ML training data is broken into a plurality of non-overlapping subsets of features and observations. A first ML algorithm is trained based on a first subset of the features and observations, and a second ML algorithm is trained based on a second subset of the features and observations. The training of the first ML algorithm overlaps in time with the training of the second ML algorithm. The first and second ML algorithms are tested. Either the first or second ML algorithm is selected based at least in part on results of the testing. The selected ML algorithm is retained as a trained ML algorithm for predicting one or more of the plurality of features based on one or more others of the plurality of features.

## Claims

### Claim 1

A computer-implemented method comprising:
receiving machine learning (ML) training data comprising a plurality of features for a plurality of observations;
breaking the ML training data into a plurality of non-overlapping subsets of features and observations;
training a first ML algorithm based on a first subset of the features and observations, and a second ML algorithm based on a second subset of the features and observations, the training of the first ML algorithm overlapping in time with the training of the second ML algorithm;
testing the first ML algorithm and the second ML algorithm;
selecting one of the first ML algorithm and the second ML algorithm based at least in part on results of the testing; and
retaining the selected ML algorithm as a trained ML algorithm for predicting one or more of the plurality of features based on one or more other features of the other plurality of features.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein the results of the testing include an error level.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein the first and second subset include a same subset of the plurality of features.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3, wherein the training, testing, selecting, and retaining are repeated for multiple different subsets of the plurality of features.

### Claim 5 (depends on 3)

The computer-implemented method of claim 3, wherein the training, testing, selecting, and retaining are repeated for all subsets of the plurality of features.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein the testing the first ML algorithm overlaps in time with the testing of the second ML algorithm.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein the training and testing are ML algorithm agnostic.

### Claim 8 (depends on 1)

The computer-implemented method of claim 1, wherein the training and testing are repeated until a user defined error threshold is reached.

### Claim 9

A system comprising:
a memory having computer readable instructions; and
one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:
receiving machine learning (ML) training data comprising a plurality of features for a plurality of observations;
breaking the ML training data into a plurality of non-overlapping subsets of features and observations;
training a first ML algorithm based on a first subset of the features and observations, and a second ML algorithm based on a second subset of the features and observations, the training of the first ML algorithm overlapping in time with the training of the second ML algorithm;
testing the first ML algorithm and the second ML algorithm;
selecting one of the first ML algorithm and the second ML algorithm based at least in part on results of the testing; and
retaining the selected ML algorithm as a trained ML algorithm for predicting one or more of the plurality of features based on one or more other features of the other plurality of features.

### Claim 10 (depends on 9)

The system of claim 9, wherein the results of the testing include an error level.

### Claim 11 (depends on 9)

The system of claim 9, wherein the first and second subset include a same subset of the plurality of features.

### Claim 12 (depends on 11)

The system of claim 11, wherein the training, testing, selecting, and retaining are repeated for multiple different subsets of the plurality of features.

### Claim 13 (depends on 12)

The system of claim 12, wherein the training, testing, selecting, and retaining are repeated for all subsets of the plurality of features.

### Claim 14 (depends on 12)

The system of claim 12, wherein the testing the first ML algorithm overlaps in time with the testing of the second ML algorithm.

### Claim 15 (depends on 9)

The system of claim 9, wherein the training and testing are ML algorithm agnostic.

### Claim 16 (depends on 9)

The system of claim 9, wherein the training and testing are repeated until a user defined error threshold is reached.

### Claim 17

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:
receiving machine learning (ML) training data comprising a plurality of features for a plurality of observations;
breaking the ML training data into a plurality of non-overlapping subsets of features and observations;
training a first ML algorithm based on a first subset of the features and observations, and a second ML algorithm based on a second subset of the features and observations, the training of the first ML algorithm overlapping in time with the training of the second ML algorithm;
testing the first ML algorithm and the second ML algorithm;
selecting one of the first ML algorithm and the second ML algorithm based at least in part on results of the testing; and
retaining the selected ML algorithm as a trained ML algorithm for predicting one or more of the plurality of features based on one or more other features of the other plurality of features.

### Claim 18 (depends on 17)

The computer program product of claim 17, wherein the results of the testing include an error level.

### Claim 19 (depends on 17)

The computer program product of claim 17, wherein the first and second subset include a same subset of the plurality of features.

### Claim 20 (depends on 19)

The computer program product of claim 19, wherein the training, testing, selecting, and retaining are repeated for multiple different subsets of the plurality of features.