Multiple Instance Learning with Bag-Level Randomized Trees
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00507111" target="_blank" >RIV/67985556:_____/19:00507111 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-10925-7_16" target="_blank" >http://dx.doi.org/10.1007/978-3-030-10925-7_16</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-10925-7_16" target="_blank" >10.1007/978-3-030-10925-7_16</a>
Alternative languages
Result language
angličtina
Original language name
Multiple Instance Learning with Bag-Level Randomized Trees
Original language description
Knowledge discovery in databases with a flexible structure poses a great challenge to machine learning community. Multiple Instance Learning (MIL) aims at learning from samples (called bags) represented by multiple feature vectors (called instances) as opposed to single feature vectors characteristic for the traditional data representation. This relaxation turns out to be useful in formulating many machine learning problems including classification of molecules, cancer detection from tissue images or identification of malicious network communications. However, despite the recent progress in this area, the current set of MIL tools still seems to be very application specific and/or burdened with many tuning parameters or processing steps. In this paper, we propose a simple, yet effective tree-based algorithm for solving MIL classification problems. Empirical evaluation against 28 classifiers on 29 publicly available benchmark datasets shows a high level performance of the proposed solution even with its default parameter settings.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases
ISBN
978-3-030-10925-7
ISSN
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e-ISSN
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Number of pages
14
Pages from-to
259-272
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Dublin
Event date
Sep 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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