SMITH: A Self-supervised Downstream-Aware Framework for Missing Testing Data Handling
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
13281 LNAI
Pages
499-510
Date Issued
2022
Author(s)
Abstract
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Subjects
Downstream-aware; Missing testing data; Self-supervised learning; Tabular data; Transformer
Other Subjects
Electric transformer testing; Machine learning; Down-stream; Downstream-aware; Machine learning communities; Missing testing data; Missing values; Performance; Self-supervised learning; Tabular data; Testing data; Transformer; Data handling
Type
conference paper