About this scholarly article

Table Enrichment System for Machine Learning by Yuyang Dong; Masafumi Oyamada is a scholarly article available to read on EtoBox.

Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and improves the accuracy of machine learning predictive models. Our system has four stages, join row search, task-related table selection, row and column alignment, and feature selection and evaluation, to efficiently create an enriched table for a given query table and a specified machine learning task. We demonstrate our system with a web UI to show the use cases of table enrichment. CCS CONCEPTS • Information systems → Data extraction and integration; Web searching and information discovery.

Author

Yuyang Dong; Masafumi Oyamada

Publisher

ACM

Published

2022

Language

EN