公开(公告)号：US11861469B2

公开(公告)日：2024-01-02

申请号：US16919258

申请日：2020-07-02

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Peter Daniel Kirchner ,  Gregory Bramble ,  Horst Cornelius Samulowitz ,  Dakuo Wang ,  Arunima Chaudhary ,  Gregory Filla

IPC: G06N20/00 ,  G06F8/35 ,  G06F8/76

CPC classification number: G06N20/00 ,  G06F8/35 ,  G06F8/76

Abstract: An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

公开(公告)号：US20230289277A1

公开(公告)日：2023-09-14

申请号：US17692268

申请日：2022-03-11

Applicant: International Business Machines Corporation

Inventor： Saket K. Sathe ,  Long VU ,  Peter Daniel Kirchner ,  Horst Cornelius Samulowitz

IPC: G06F11/34

CPC classification number: G06F11/3452 ,  G06F11/3428 ,  G06N20/00

Abstract: Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.

公开(公告)号：US20220164698A1

公开(公告)日：2022-05-26

申请号：US17104642

申请日：2020-11-25

Applicant: International Business Machines Corporation

Inventor： Arunima Chaudhary ,  Dakuo Wang ,  Abel Valente ,  Carolina Maria Spina ,  Hima Patel ,  Nitin Gupta ,  Gregory Bramble ,  Horst Cornelius Samulowitz ,  Sameep Mehta ,  Theodoros Salonidis ,  Daniel M. Gruen ,  Chaung Gan

IPC: G06N20/00 ,  G06N5/04

Abstract: A method to automatically assess data quality of data input into a machine learning model and remediate the data includes receiving input data for an automated machine learning model. Selections for a multiple data quality metrics are displayed. A selection for data quality metrics is received. The data quality metrics are determined according to the selection. Selections for data remediation strategies based on the selection of the data quality metrics are displayed. A selection for remediation recommendation strategies is received. The selected data remediation strategies are performed on the input data. Learning from the selection of the data quality metrics and the selection for the remediation strategies is performed. A new customized machine learning model is generated based on the learning.

公开(公告)号：US11275974B2

公开(公告)日：2022-03-15

申请号：US16133583

申请日：2018-09-17

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Saket Sathe ,  Deepak S. Turaga ,  Horst Cornelius Samulowitz ,  Charu C. Aggarwal

IPC: G06K9/22 ,  G06F16/90 ,  G06K9/62 ,  G06F16/901 ,  G06N20/00

Abstract: Embodiments for automated feature engineering by one or more processors are described. One or more selected transformations may be applied to a set of features in a dataset to create a set of transform features using random feature transformation forest (RFTF) classifiers. A transform feature may be selected from the set of transform features having a highest discriminative power as compared to other features of the set of transform features. At each node in a decision tree, store the selected feature, a split value, and the one or more selected transformations for the transform feature.

公开(公告)号：US11048718B2

公开(公告)日：2021-06-29

申请号：US15673812

申请日：2017-08-10

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Elias Khalil ,  Udayan Khurana ,  Fatemeh Nargesian ,  Horst Cornelius Samulowitz ,  Deepak S. Turaga

IPC: G06F16/00 ,  G06F16/25 ,  G06F16/21 ,  G06F16/28

Abstract: Embodiments for feature engineering by one or more processors are described. A plurality of transformations are applied to a set of features in each of a plurality of datasets. An output of each of the plurality of transformations is a score. For each of the sets of features, selecting those of the plurality of transformations for which said score is above a predetermined threshold. A signal representative of said selection is generated.

公开(公告)号：US20240428124A1

公开(公告)日：2024-12-26

申请号：US18338671

申请日：2023-06-21

Applicant: International Business Machines Corporation

Inventor： Long Vu ,  Peter Daniel Kirchner ,  Horst Cornelius Samulowitz ,  Charu C. Aggarwal

IPC: G06N20/00

Abstract: Embodiments of the invention are directed to a computer system including a memory communicatively coupled to a processor system. The processor system is operable to perform processor system operations that include using a first machine learning (ML) algorithm to convert to-be-classified-data (TBC-data) from a TBC-data format to a second data format; and extract features from the TBC-data in the second data format. A second ML algorithm is used to perform a task that includes determining, based at least in part on the features of the TBC-data in the second data format, that the TBC-data having the second data format is an outlier.

公开(公告)号：US11966340B2

公开(公告)日：2024-04-23

申请号：US17654965

申请日：2022-03-15

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Long Vu ,  Bei Chen ,  Xuan-Hong Dang ,  Peter Daniel Kirchner ,  Syed Yousaf Shah ,  Dhavalkumar C. Patel ,  Si Er Han ,  Ji Hui Yang ,  Jun Wang ,  Jing James Xu ,  Dakuo Wang ,  Gregory Bramble ,  Horst Cornelius Samulowitz ,  Saket K. Sathe ,  Wesley M. Gifford ,  Petros Zerfos

IPC: G06F12/0871 ,  G06N20/00

CPC classification number: G06F12/0871 ,  G06N20/00 ,  G06F2212/604

Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

公开(公告)号：US11868230B2

公开(公告)日：2024-01-09

申请号：US17692268

申请日：2022-03-11

Applicant: International Business Machines Corporation

Inventor： Saket K. Sathe ,  Long Vu ,  Peter Daniel Kirchner ,  Horst Cornelius Samulowitz

IPC: G06F11/34 ,  G06N20/00

CPC classification number: G06F11/3452 ,  G06F11/3428 ,  G06N20/00

Abstract: Computer hardware and/or software that performs the following operations: (i) assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets; (ii) associating the performance with meta-features corresponding to respective pipeline/data set combinations; (iii) training a supervised meta-learning model using the associated performance and meta-features as training data; and (iv) utilizing the trained model to identify one or more pipelines for processing an input data set.

公开(公告)号：US20230177032A1

公开(公告)日：2023-06-08

申请号：US17545880

申请日：2021-12-08

Applicant: International Business Machines Corporation

Inventor： Daniel Karl I. Weidele ,  Lisa Amini ,  Udayan Khurana ,  Kavitha Srinivas ,  Horst Cornelius Samulowitz ,  Takaaki Tateishi ,  Carolina Maria Spina ,  Dakuo Wang ,  Abel Valente ,  Arunima Chaudhary ,  Toshihiro Takahashi

IPC: G06F16/22 ,  G06F16/2457 ,  G06F16/28

CPC classification number: G06F16/221 ,  G06F16/2457 ,  G06F16/288 ,  G06F16/2282

Abstract: A computer-implemented method according to one embodiment includes identifying a data set and meta information; and augmenting the data set with additional features in response to an automatic analysis of the data set in view of the meta information.

公开(公告)号：US11620582B2

公开(公告)日：2023-04-04

申请号：US16942247

申请日：2020-07-29

Applicant: International Business Machines Corporation

Inventor： Bei Chen ,  Long Vu ,  Syed Yousaf Shah ,  Xuan-Hong Dang ,  Peter Daniel Kirchner ,  Si Er Han ,  Ji Hui Yang ,  Jun Wang ,  Jing James Xu ,  Dakuo Wang ,  Dhavalkumar C. Patel ,  Gregory Bramble ,  Horst Cornelius Samulowitz ,  Saket Sathe ,  Chuang Gan

IPC: G06N20/20

Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.