APTER:: Aggregated Prognosis Through Exponential Re-weighting

Yang Liu; Kristiaan Pelckmans

doi:10.1007/978-3-030-26176-4_35

APTER: Aggregated Prognosis Through Exponential Re-weighting

Yang Liu, Kristiaan Pelckmans

School of Computing, Engineering & Digital Technologies

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels. The method is an application of the aggregation method as recently proposed in the literature on theoretical machine learning, and excels in its computational convenience and capability to deal with high-dimensional data. This paper gives a formal analysis of the method, yielding rates of convergence similar to what traditional techniques obtain, while it is shown to cope well with an exponentially large set of features. Those results are supported by numerical simulations on a range of publicly available survival-micro-array data sets. It is empirically found that the proposed technique combined with a recently proposed pre-processing technique gives excellent performances. All used software files and data sets are available on the authors’ website http://user.it.uu.se/~liuya610/index.html.

Original language	English
Title of host publication	Lecture Notes in Computer Science
Subtitle of host publication	including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
Publisher	Springer
Pages	425-436
Volume	11653 LNCS
DOIs	https://doi.org/10.1007/978-3-030-26176-4_35
Publication status	E-pub ahead of print - 21 Jul 2019
Event	25th International Computing and Combinatorics Conference - Xi'an, China Duration: 29 Jul 2019 → 31 Jul 2019

Publication series

Name	Computing and Combinatorics
Volume	11653
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Computing and Combinatorics Conference
Abbreviated title	COCOON 2019
Country/Territory	China
City	Xi'an
Period	29/07/19 → 31/07/19

Access to Document

10.1007/978-3-030-26176-4_35Licence: Unspecified

http://link.springer.com/10.1007/978-3-030-26176-4_35Licence: Unspecified

Cite this

Liu, Y., & Pelckmans, K. (2019). APTER: Aggregated Prognosis Through Exponential Re-weighting. In Lecture Notes in Computer Science: including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics (Vol. 11653 LNCS, pp. 425-436). Article Chapter 35 (Computing and Combinatorics; Vol. 11653). Springer. Advance online publication. https://doi.org/10.1007/978-3-030-26176-4_35

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abstract = " This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels. The method is an application of the aggregation method as recently proposed in the literature on theoretical machine learning, and excels in its computational convenience and capability to deal with high-dimensional data. This paper gives a formal analysis of the method, yielding rates of convergence similar to what traditional techniques obtain, while it is shown to cope well with an exponentially large set of features. Those results are supported by numerical simulations on a range of publicly available survival-micro-array data sets. It is empirically found that the proposed technique combined with a recently proposed pre-processing technique gives excellent performances. All used software files and data sets are available on the authors{\textquoteright} website http://user.it.uu.se/~liuya610/index.html.",

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series = "Computing and Combinatorics",

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Liu, Y & Pelckmans, K 2019, APTER: Aggregated Prognosis Through Exponential Re-weighting. in Lecture Notes in Computer Science: including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. vol. 11653 LNCS, Chapter 35, Computing and Combinatorics, vol. 11653, Springer, pp. 425-436, 25th International Computing and Combinatorics Conference, Xi'an, China, 29/07/19. https://doi.org/10.1007/978-3-030-26176-4_35

APTER: Aggregated Prognosis Through Exponential Re-weighting. / Liu, Yang; Pelckmans, Kristiaan.
Lecture Notes in Computer Science: including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. Vol. 11653 LNCS Springer, 2019. p. 425-436 Chapter 35 (Computing and Combinatorics; Vol. 11653).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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AB - This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels. The method is an application of the aggregation method as recently proposed in the literature on theoretical machine learning, and excels in its computational convenience and capability to deal with high-dimensional data. This paper gives a formal analysis of the method, yielding rates of convergence similar to what traditional techniques obtain, while it is shown to cope well with an exponentially large set of features. Those results are supported by numerical simulations on a range of publicly available survival-micro-array data sets. It is empirically found that the proposed technique combined with a recently proposed pre-processing technique gives excellent performances. All used software files and data sets are available on the authors’ website http://user.it.uu.se/~liuya610/index.html.

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