Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening

Ning Situ, Xiaojing Yuan, George Zouridakis
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:688-697, 2011.

Abstract

In typical classification problems, high level concept features provided by a domain expert are usually available during classifier training but not during its deployment. We address this problem from a multitask learning (MTL) perspective by treating these features as auxiliary learning tasks. Previous efforts in MTL have mostly assumed that all tasks have the same input space. However, auxiliary tasks can have different input spaces, since their learning targets are different. Thus, to handle cases with heterogeneous input, in this paper we present a newly developed model using heterogeneous auxiliary tasks to help main task learning. First, we formulate a convex optimization problem for the proposed model, and then, we analyze its hypothesis class and derive true risk bounds. Finally, we compare the proposed model with other relevant methods when applied to the problem of skin cancer screening and public datasets. Our results show that the performance of the proposed method is highly competitive compared to other relevant methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-situ11a, title = {Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening}, author = {Situ, Ning and Yuan, Xiaojing and Zouridakis, George}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {688--697}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/situ11a/situ11a.pdf}, url = {https://proceedings.mlr.press/v15/situ11a.html}, abstract = {In typical classification problems, high level concept features provided by a domain expert are usually available during classifier training but not during its deployment. We address this problem from a multitask learning (MTL) perspective by treating these features as auxiliary learning tasks. Previous efforts in MTL have mostly assumed that all tasks have the same input space. However, auxiliary tasks can have different input spaces, since their learning targets are different. Thus, to handle cases with heterogeneous input, in this paper we present a newly developed model using heterogeneous auxiliary tasks to help main task learning. First, we formulate a convex optimization problem for the proposed model, and then, we analyze its hypothesis class and derive true risk bounds. Finally, we compare the proposed model with other relevant methods when applied to the problem of skin cancer screening and public datasets. Our results show that the performance of the proposed method is highly competitive compared to other relevant methods.} }
Endnote
%0 Conference Paper %T Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening %A Ning Situ %A Xiaojing Yuan %A George Zouridakis %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-situ11a %I PMLR %P 688--697 %U https://proceedings.mlr.press/v15/situ11a.html %V 15 %X In typical classification problems, high level concept features provided by a domain expert are usually available during classifier training but not during its deployment. We address this problem from a multitask learning (MTL) perspective by treating these features as auxiliary learning tasks. Previous efforts in MTL have mostly assumed that all tasks have the same input space. However, auxiliary tasks can have different input spaces, since their learning targets are different. Thus, to handle cases with heterogeneous input, in this paper we present a newly developed model using heterogeneous auxiliary tasks to help main task learning. First, we formulate a convex optimization problem for the proposed model, and then, we analyze its hypothesis class and derive true risk bounds. Finally, we compare the proposed model with other relevant methods when applied to the problem of skin cancer screening and public datasets. Our results show that the performance of the proposed method is highly competitive compared to other relevant methods.
RIS
TY - CPAPER TI - Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening AU - Ning Situ AU - Xiaojing Yuan AU - George Zouridakis BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-situ11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 688 EP - 697 L1 - http://proceedings.mlr.press/v15/situ11a/situ11a.pdf UR - https://proceedings.mlr.press/v15/situ11a.html AB - In typical classification problems, high level concept features provided by a domain expert are usually available during classifier training but not during its deployment. We address this problem from a multitask learning (MTL) perspective by treating these features as auxiliary learning tasks. Previous efforts in MTL have mostly assumed that all tasks have the same input space. However, auxiliary tasks can have different input spaces, since their learning targets are different. Thus, to handle cases with heterogeneous input, in this paper we present a newly developed model using heterogeneous auxiliary tasks to help main task learning. First, we formulate a convex optimization problem for the proposed model, and then, we analyze its hypothesis class and derive true risk bounds. Finally, we compare the proposed model with other relevant methods when applied to the problem of skin cancer screening and public datasets. Our results show that the performance of the proposed method is highly competitive compared to other relevant methods. ER -
APA
Situ, N., Yuan, X. & Zouridakis, G.. (2011). Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:688-697 Available from https://proceedings.mlr.press/v15/situ11a.html.

Related Material