Submodularity in Data Subset Selection and Active Learning

Kai Wei, Rishabh Iyer, Jeff Bilmes
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1954-1963, 2015.

Abstract

We study the problem of selecting a subset of big data to train a classifier while incurring minimal performance loss. We show the connection of submodularity to the data likelihood functions for Naive Bayes (NB) and Nearest Neighbor (NN) classifiers, and formulate the data subset selection problems for these classifiers as constrained submodular maximization. Furthermore, we apply this framework to active learning and propose a novel scheme filtering active submodular selection (FASS), where we combine the uncertainty sampling method with a submodular data subset selection framework. We extensively evaluate the proposed framework on text categorization and handwritten digit recognition tasks with four different classifiers, including Deep Neural Network (DNN) based classifiers. Empirical results indicate that the proposed framework yields significant improvement over the state-of-the-art algorithms on all classifiers.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-wei15, title = {Submodularity in Data Subset Selection and Active Learning}, author = {Wei, Kai and Iyer, Rishabh and Bilmes, Jeff}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {1954--1963}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/wei15.pdf}, url = {https://proceedings.mlr.press/v37/wei15.html}, abstract = {We study the problem of selecting a subset of big data to train a classifier while incurring minimal performance loss. We show the connection of submodularity to the data likelihood functions for Naive Bayes (NB) and Nearest Neighbor (NN) classifiers, and formulate the data subset selection problems for these classifiers as constrained submodular maximization. Furthermore, we apply this framework to active learning and propose a novel scheme filtering active submodular selection (FASS), where we combine the uncertainty sampling method with a submodular data subset selection framework. We extensively evaluate the proposed framework on text categorization and handwritten digit recognition tasks with four different classifiers, including Deep Neural Network (DNN) based classifiers. Empirical results indicate that the proposed framework yields significant improvement over the state-of-the-art algorithms on all classifiers.} }
Endnote
%0 Conference Paper %T Submodularity in Data Subset Selection and Active Learning %A Kai Wei %A Rishabh Iyer %A Jeff Bilmes %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-wei15 %I PMLR %P 1954--1963 %U https://proceedings.mlr.press/v37/wei15.html %V 37 %X We study the problem of selecting a subset of big data to train a classifier while incurring minimal performance loss. We show the connection of submodularity to the data likelihood functions for Naive Bayes (NB) and Nearest Neighbor (NN) classifiers, and formulate the data subset selection problems for these classifiers as constrained submodular maximization. Furthermore, we apply this framework to active learning and propose a novel scheme filtering active submodular selection (FASS), where we combine the uncertainty sampling method with a submodular data subset selection framework. We extensively evaluate the proposed framework on text categorization and handwritten digit recognition tasks with four different classifiers, including Deep Neural Network (DNN) based classifiers. Empirical results indicate that the proposed framework yields significant improvement over the state-of-the-art algorithms on all classifiers.
RIS
TY - CPAPER TI - Submodularity in Data Subset Selection and Active Learning AU - Kai Wei AU - Rishabh Iyer AU - Jeff Bilmes BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-wei15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 1954 EP - 1963 L1 - http://proceedings.mlr.press/v37/wei15.pdf UR - https://proceedings.mlr.press/v37/wei15.html AB - We study the problem of selecting a subset of big data to train a classifier while incurring minimal performance loss. We show the connection of submodularity to the data likelihood functions for Naive Bayes (NB) and Nearest Neighbor (NN) classifiers, and formulate the data subset selection problems for these classifiers as constrained submodular maximization. Furthermore, we apply this framework to active learning and propose a novel scheme filtering active submodular selection (FASS), where we combine the uncertainty sampling method with a submodular data subset selection framework. We extensively evaluate the proposed framework on text categorization and handwritten digit recognition tasks with four different classifiers, including Deep Neural Network (DNN) based classifiers. Empirical results indicate that the proposed framework yields significant improvement over the state-of-the-art algorithms on all classifiers. ER -
APA
Wei, K., Iyer, R. & Bilmes, J.. (2015). Submodularity in Data Subset Selection and Active Learning. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:1954-1963 Available from https://proceedings.mlr.press/v37/wei15.html.

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