Systems and methods for automated machine learning

Patent metadata

Field	Value
Publication number	US-2021150412-A1
Application number	US-202017100082-A
Country	US
Kind code	A1
Filing date	Nov 20, 2020
Priority date	Nov 20, 2019
Publication date	May 20, 2021
Grant date	—

Abstract

In some aspects, the disclosure is directed to methods and systems for automatic machine learning through a combination of unsupervised and supervised machine learning from a large set of machine learning algorithms and feature selectors and transformers to generate a plurality of machine learning models, each associated with a particular combination of features and hyperparameters. Each machine learning model is trained and assessed to identify the best performing model based on one or more specified statistical measures. An application may be automatically constructed based on a selected model to process further input data.

First claim

We claim:

1 . A method for automatic generation of machine learning applications, comprising:
receiving, by a computing device, input data; identifying, by the computing device, a plurality of feature sets by determining correlations or covariances between combinations of features in the input data; generating, by the computing device, a plurality of hyperparameter sets; generating, by the computing device, a plurality of machine learning models, each machine learning model utilizing one of the plurality of feature sets and one of the plurality of hyperparameter sets; training, by the computing device, each of the plurality of machine learning models using a first subset of the input data; scoring, by the computing device, each of the plurality of machine learning models using a second subset of the input data; receiving a selection, by the computing device, of a first machine learning model of the scored plurality of machine learning models; and generating an application, by the computing device, the application executing the first machine learning model.

2 . The method of claim 1 , further comprising scaling the input data to a predetermined range.

3 . The method of claim 2 , wherein the input data comprises a plurality of feature types, and wherein scaling the input data further comprises scaling input data of each feature type of the plurality of feature types to a predetermined range associated with the corresponding feature type.

4 . The method of claim 1 , further comprising splitting, by the computing device, the input data into the first subset of data and the second subset of data.

5 . The method of claim 4 , wherein the first subset of data is balanced for a first feature of the features in the input data.

6 . The method of claim 1 , wherein generating the plurality of hyperparameter sets further comprises generating, via one of a custom grid search or a random search tool, the plurality of hyperparameter sets, each set of hyperparameters distinct from each other set of hyperparameters.

7 . The method of claim 6 , wherein generating the plurality of hyperparameter sets further comprises generating a plurality of values for each hyperparameter, the plurality of values distributed across a predetermined range; and selecting a value for each hyperparameter of a corresponding machine learning model from the generated plurality of values.

8 . The method of claim 1 , wherein the plurality of machine learning models comprise at least one machine learning model of a first type and at least one machine learning model of a different second type.

9 . The method of claim 8 , wherein the first type and second type comprise different ones of a decision tree, a gradient boosting machine, a k-nearest neighbor algorithm, a support vector machine, a random forest algorithm, and a neural network.

10 . A system for automatic generation of machine learning applications, comprising:
a computing device comprising a memory storing input data, and a processor configured to:
identify a plurality of feature sets by determining correlations or covariances between combinations of features in the input data,
generate a plurality of hyperparameter sets,
generate a plurality of machine learning models, each machine learning model utilizing one of the plurality of feature sets and one of the plurality of hyperparameter sets,
train each of the plurality of machine learning models using a first subset of the input data,
score each of the plurality of machine learning models using a second subset of the input data,
receive a selection of a first machine learning model of the scored plurality of machine learning models, and
generate an application, the application executing the first machine learning model.

11 . The system of claim 10 , wherein the processor is further configured to scale the input data to a predetermined range.

12 . The system of claim 11 , wherein the input data comprises a plurality of feature types, and wherein the processor is further configured to scale input data of each feature type of the plurality of feature types to a predetermined range associated with the corresponding feature type.

13 . The system of claim 10 , wherein the processor is further configured to split the input data into the first subset of data and the second subset of data.

14 . The system of claim 13 , wherein the first subset of data is balanced for a first feature of the features in the input data.

15 . The system of claim 10 , wherein the processor is further configured to generate, via one of a custom grid search or a random search tool, the plurality of hyperparameter sets, each set of hyperparameters distinct from each other set of hyperparameters.

16 . The system of claim 15 , wherein the processor is further configured to generate a plurality of values for each hyperparameter, the plurality of values distributed across a predetermined range; and select a value for each hyperparameter of a corresponding machine learning model from the generated plurality of values.

17 . The system of claim 10 , wherein the plurality of machine learning models comprise at least one machine learning model of a first type and at least one machine learning model of a different second type.

18 . The system of claim 17 , wherein the first type and second type comprise different ones of a decision tree, a gradient boosting machine, a k-nearest neighbor algorithm, a support vector machine, a random forest algorithm, and a neural network.

19 . A non-transitory computer readable medium storing instructions that, when executed by a processor of a computing device, cause the computing device to:
identify a plurality of feature sets by determining correlations or covariances between combinations of features in a set of received input data; generate a plurality of hyperparameter sets; generate a plurality of machine learning models, each machine learning model utilizing one of the plurality of feature sets and one of the plurality of hyperparameter sets; train each of the plurality of machine learning models using a first subset of the input data; score each of the plurality of machine learning models using a second subset of the input data; receive a selection of a first machine learning model of the scored plurality of machine learning models; and generate an application, the application executing the first machine learning model.

20 . The computer readable medium of claim 19 , wherein the instructions further comprise instructions that cause the computing device to generate, via one of a custom grid search or a random search tool, the plurality of hyperparameter sets, each set of hyperparameters distinct from each other set of hyperparameters.

Assignees

• Univ California

Inventors

• Rashidi Hooman H
• Albahra Samer
• Tran Nam

Classifications

• G06N5/01

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

• G06N3/09

  Supervised learning · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N20/10 Primary

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06N3/08

  Learning methods · CPC title