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An Evaluation of Self-training Styles for Domain Adaptation on the Task of Splice Site Prediction

Authors:

Doina CarageaAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1042 - 1047

https://doi.org/10.1145/2808797.2808809

Published: 25 August 2015 Publication History

Abstract

We consider the problem of adding a large unlabeled sample from the target domain to boost the performance of a domain adaptation algorithm when only a small set of labeled examples are available from the target domain. In particular, we consider the problem setting motivated by the task of splice site prediction. For this task, annotating a genome using machine learning requires a lot of labeled data, whereas for non-model organisms, there is only some labeled data and lots of unlabeled data. With domain adaptation one can leverage the large amount of data from a related model organism, along with the labeled and unlabeled data from the organism of interest to train a classifier for the latter. Our goal is to analyze the three ways of incorporating the unlabeled data -- with soft labels only (i.e., Expectation-Maximization), with hard labels only (i.e., self-training), or with both soft and hard labels -- for the splice site prediction in particular, and more broadly for a general iterative domain adaptation setting. We provide empirical results on splice site prediction indicating that using soft labels only can lead to better classifier compared to the other two ways.

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Cited By

Joshi VAnish PGhaisas SSpinellis DGousios GChechik MDi Penta M(2021)Domain adaptation for an automated classification of deontic modalities in software engineering contractsProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3473921(1275-1280)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3473921
Li HCaragea DCaragea CHerndon N(2017)Disaster response aided by tweet classification with a domain adaptation approachJournal of Contingencies and Crisis Management10.1111/1468-5973.1219426:1(16-27)Online publication date: 4-Sep-2017
https://doi.org/10.1111/1468-5973.12194

An Evaluation of Self-training Styles for Domain Adaptation on the Task of Splice Site Prediction

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

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Published: 25 August 2015

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ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

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Cited By

Joshi VAnish PGhaisas SSpinellis DGousios GChechik MDi Penta M(2021)Domain adaptation for an automated classification of deontic modalities in software engineering contractsProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3473921(1275-1280)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3473921
Li HCaragea DCaragea CHerndon N(2017)Disaster response aided by tweet classification with a domain adaptation approachJournal of Contingencies and Crisis Management10.1111/1468-5973.1219426:1(16-27)Online publication date: 4-Sep-2017
https://doi.org/10.1111/1468-5973.12194

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