US 12,412,102 B2
Interactive feature engineering in automatic machine learning with domain knowledge
Dakuo Wang, Cambridge, MA (US); Udayan Khurana, White Plains, NY (US); Daniel Karl I. Weidele, Cambridge, MA (US); Arunima Chaudhary, Boston, MA (US); Carolina Maria Spina, Olavarria (AR); Abel Valente, Villa Elisa (AR); Chuang Gan, Cambridge, MA (US); Horst Cornelius Samulowitz, Armonk, NY (US); and Lisa Amini, Weston, MA (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on May 11, 2021, as Appl. No. 17/317,242.
Prior Publication US 2022/0366269 A1, Nov. 17, 2022
Int. Cl. G06N 5/022 (2023.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)
CPC G06N 5/022 (2013.01) [G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, by at least one processor performing feature engineering in an automatic machine learning pipeline, a dataset including features;
mapping, by the at least one processor, the features in the dataset to nodes of a knowledge graph, the nodes representing respective concepts, the knowledge graph further including edges connecting the nodes and representing respective relationships between respective two of the nodes that a respective edge connects, wherein the mapping occurs based on the concept represented by the corresponding node;
traversing, by the at least one processor, the knowledge graph to find a candidate node existing in the knowledge graph and connected to at least one mapped node that is mapped to at least one feature in the dataset, the candidate node not being mapped to any of the features in the dataset;
identifying, by the at least one processor, a concept associated with the candidate node as a new feature;
presenting a user interface that includes the new feature and the respective concepts mapped to the features as user-engageable elements that are changeable via user input; and
receiving, via user input into one or more of the user-engageable elements of the user interface, a change for the new feature,
wherein the automatic machine learning pipeline uses the features in the dataset and the changed new feature to select a subset of features for training a machine learning model.