SMITH: A Self-supervised Downstream-Aware Framework for Missing Testing Data Handling

Chih Chun Yang, Cheng Te Li, Shou De Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Missing values in testing data has been a notorious problem in machine learning community since it can heavily deteriorate the performance of downstream model learned from complete data without any precaution. To better perform the prediction task with this kind of downstream model, we must impute the missing value first. Therefore, the imputation quality and how to utilize the knowledge provided by the pre-trained and fixed downstream model are the keys to address this problem. In this paper, we aim to address this problem and focus on models learned from tabular data. We present a novel Self-supervised downstream-aware framework for MIssing Testing data Handling (SMITH), which consists of a transformer-based imputation model and a downstream label estimation algorithm. The former can be replaced by any existing imputation model of interest with additional performance gain acquired in comparison with that of their original design. By advancing two self-supervised tasks and the knowledge from the prediction of the downstream model to guide the learning of our transformer-based imputation model, our SMITH framework performs favorably against state-of-the-art methods under several benchmarking datasets.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
EditorsJoão Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031059353
Publication statusPublished - 2022
Event26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022 - Chengdu, China
Duration: 2022 May 162022 May 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13281 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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