APTER: Aggregated Prognosis Through Exponential Re-weighting

Yang Liu, Kristiaan Pelckmans

Research output: Chapter in Book/Report/Conference proceedingChapter

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 languageEnglish
Title of host publicationLecture Notes in Computer Science
Subtitle of host publicationincluding subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
PublisherSpringer
Pages425-436
Volume11653 LNCS
DOIs
Publication statusE-pub ahead of print - 21 Jul 2019
Event25th International Computing and Combinatorics Conference - Xi'an, China
Duration: 29 Jul 201931 Jul 2019

Publication series

NameComputing and Combinatorics
Volume11653
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Computing and Combinatorics Conference
Abbreviated titleCOCOON 2019
CountryChina
CityXi'an
Period29/07/1931/07/19

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  • 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). [Chapter 35] (Computing and Combinatorics; Vol. 11653). Springer. https://doi.org/10.1007/978-3-030-26176-4_35