research-article

Uncertainty sampling and transductive experimental design for active dual supervision

Authors:

Vikas Sindhwani,

Prem Melville,

Richard D. LawrenceAuthors Info & Claims

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

Pages 953 - 960

https://doi.org/10.1145/1553374.1553496

Published: 14 June 2009 Publication History

Get Access

Abstract

Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features. Labeled features are naturally available in tasks such as text classification where it is frequently possible to provide domain knowledge in the form of words that associate strongly with a class. In this paper, we consider the novel problem of active dual supervision, or, how to optimally query an example and feature labeling oracle to simultaneously collect two different forms of supervision, with the objective of building the best classifier in the most cost effective manner. We apply classical uncertainty and experimental design based active learning schemes to graph/kernel-based dual supervision models. Empirical studies confirm the potential of these schemes to significantly reduce the cost of acquiring labeled data for training high-quality models.

References

[1]

Belkin, M., Matveeva, I., & Niyogi, P. (2004). Regularization and semi-supervised learning on large graphs. Conference on Learning Theory (COLT) (pp. 486--500).

Crossref

Google Scholar

[2]

Druck, G., Mann, G., & McCallum, A. (2008). Learning from labeled features using generalized expectation criteria. 31st Annual ACM SIGIR Conference (pp. 595--602).

Digital Library

Google Scholar

[3]

Globerson, A., Chechik, G., Pereira, F., & Tishby, N. (2007). Euclidean embedding of co-occurence data. Journal of Machine Learning Research, 8, 2265--2296.

Digital Library

Google Scholar

[4]

Godbole, S., Harpale, A., Sarawagi, S., & Chakrabarti, S. (2004). Document classification through interactive supervision of document and term labels. Prin. and Prac. of Knowl. Disc. in Databases (PKDD) (pp. 185--196).

Digital Library

Google Scholar

[5]

Ho, & Dooren, P. (2005). On the pseudo-inverse of the laplacian of a bipartite graph. Appl. Math. Letters, 8, 917--922.

Crossref

Google Scholar

[6]

Melville, P., Gryc, W., & Lawrence, R. (2009). Sentiment analysis of blogs by combining lexical knowledge with text classification. 15th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining.

Digital Library

Google Scholar

[7]

Melville, P., & Sindhwani, V. (2009). Active dual supervision: Reducing the cost of annotating examples and features. NAACL HLT Workshop on Active Learning for NLP.

Digital Library

Google Scholar

[8]

Raghavan, H., Madani, O., & Jones, R. (2007). An interactive algorithm for asking and incorporating feature feedback into support vector machines. 30th Annual ACM SIGIR Conference (pp. 79--86).

Digital Library

Google Scholar

[9]

Saar-Tsechansky, M., Melville, P., & Provost, F. (2009). Active feature-value acquisition. Management Science, 4, 664--684.

Digital Library

Google Scholar

[10]

Sindhwani, V., Hu, J., & Mojsilovic, A. (2008). Regularized co-clustering with dual supervision. Neural Information Processing Systems (NIPS) (pp. 976--983).

Google Scholar

[11]

Smola, A., & Kondor, R. (2004). Kernels and regularization on graphs. Conf. on Learning Theory (COLT) (pp. 144--158).

Google Scholar

[12]

Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2, 45--66.

Digital Library

Google Scholar

[13]

Yu, K., Bi, J., & Tresp, V. (2006). Active learning via transductive experimental design. International Conference on Machine Learning (ICML) (pp. 1081--1088).

Digital Library

Google Scholar

Cited By

View all

Khorramrouz ADutta SKhudaBukhsh AElkind E(2023)For women, life, freedomProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/667(6013-6021)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/667
Dutta SSrivastava PSolunke VNath SKhudaBukhsh AElkind E(2023)Disentangling societal inequality from model biasesProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/661(5959-5967)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/661
Mai XAvestimehr SOrtega ASoltanolkotabi M(2022)On The Effectiveness of Active Learning by Uncertainty Sampling in Classification of High-Dimensional Gaussian Mixture DataICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP43922.2022.9747685(4238-4242)Online publication date: 23-May-2022
https://doi.org/10.1109/ICASSP43922.2022.9747685
Show More Cited By

Index Terms

Recommendations

Transductive Multilabel Learning via Label Set Propagation

The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image ...
Document Clustering With Dual Supervision Through Feature Reweighting

Traditional semi-supervised clustering uses only limited user supervision in the form of instance seeds for clusters and pairwise instance constraints to aid unsupervised clustering. However, user supervision can also be provided in alternative forms ...
Semi-supervised learning combining transductive support vector machine with active learning

In typical data mining applications, labeling the large amounts of data is difficult, expensive, and time consuming, if annotated manually. To avoid manual labeling, semi-supervised learning uses unlabeled data along with the labeled data in the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

June 2009

1331 pages

ISBN:9781605585161

DOI:10.1145/1553374

General Chair:
Andrea Danyluk
Williams College
,
Program Chairs:
Léon Bottou
NEC Laboratories America
,
Michael Littman
Rutgers University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

ICML '09

Sponsor:

Microsoft Research

ICML '09: The 26th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 14 - 18, 2009

Quebec, Montreal, Canada

Acceptance Rates

Overall Acceptance Rate 140 of 548 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

29
Total Citations
View Citations
301
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)1

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Khorramrouz ADutta SKhudaBukhsh AElkind E(2023)For women, life, freedomProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/667(6013-6021)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/667
Dutta SSrivastava PSolunke VNath SKhudaBukhsh AElkind E(2023)Disentangling societal inequality from model biasesProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/661(5959-5967)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/661
Mai XAvestimehr SOrtega ASoltanolkotabi M(2022)On The Effectiveness of Active Learning by Uncertainty Sampling in Classification of High-Dimensional Gaussian Mixture DataICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP43922.2022.9747685(4238-4242)Online publication date: 23-May-2022
https://doi.org/10.1109/ICASSP43922.2022.9747685
Calikus ENowaczyk SBouguelia MDikmen O(2022)Wisdom of the contexts: active ensemble learning for contextual anomaly detectionData Mining and Knowledge Discovery10.1007/s10618-022-00868-736:6(2410-2458)Online publication date: 4-Oct-2022
https://doi.org/10.1007/s10618-022-00868-7
Foggo BYu N(2021)Analyzing Data Selection Techniques with Tools from the Theory of Information Losses2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671861(7-16)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671861
Palakodety SKhudaBukhsh AJayachandran GPalakodety SKhudaBukhsh AJayachandran G(2021)Semantic SamplingLow Resource Social Media Text Mining10.1007/978-981-16-5625-5_6(49-60)Online publication date: 2-Oct-2021
https://doi.org/10.1007/978-981-16-5625-5_6
Calikus EFan YNowaczyk SSant'Anna A(2019)Interactive-COSMOProceedings of the Workshop on Interactive Data Mining10.1145/3304079.3310289(1-9)Online publication date: 15-Feb-2019
https://dl.acm.org/doi/10.1145/3304079.3310289
Sharma MBilgic M(2018)Learning with rationales for document classificationMachine Language10.1007/s10994-017-5671-3107:5(797-824)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10994-017-5671-3
Ramirez-Loaiza MSharma MKumar GBilgic M(2017)Active learningData Mining and Knowledge Discovery10.1007/s10618-016-0469-731:2(287-313)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10618-016-0469-7
Sharma MBilgic M(2017)Evidence-based uncertainty sampling for active learningData Mining and Knowledge Discovery10.1007/s10618-016-0460-331:1(164-202)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s10618-016-0460-3
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Transductive Multilabel Learning via Label Set Propagation

Document Clustering With Dual Supervision Through Feature Reweighting

Semi-supervised learning combining transductive support vector machine with active learning