International Business Machines Corporation

Arunima Chaudhary

Dakuo Wang

Horst Cornelius Samulowitz

Chuang Gan

Lisa Amini

Sijia Liu

Gregory Bramble

Deepak Vijaykeerthy

Vaibhav Saxena

Parikshit Ram

Abel Nicolas Valente

Udayan Khurana

Alexander Gray

Daniel Karl I Weidele

Carolina Maria Spina

A dataset including features and values associated with the features can be received. Each of the features in the dataset can be mapped to a corresponding node in a knowledge graph based on the concept represented by the corresponding node. The knowledge graph can be traversed to find a candidate node connected to at least one mapped node, the candidate node not being mapped to a feature in the dataset. A concept associated with the candidate node can be identified as a new feature. A machine learning model pipeline can use the features in the dataset and the new feature to select a subset of features for training a machine learning model.

Interactive feature engineering in automatic machine learning with domain knowledge

The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.

Machine learning with multiple constraints

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Boston, MA, United States of America

Arunima Chaudhary