Classification with Costly Features as a Sequential Decision-making Problem
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00339966" target="_blank" >RIV/68407700:21230/20:00339966 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s10994-020-05874-8" target="_blank" >https://doi.org/10.1007/s10994-020-05874-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10994-020-05874-8" target="_blank" >10.1007/s10994-020-05874-8</a>
Alternative languages
Result language
angličtina
Original language name
Classification with Costly Features as a Sequential Decision-making Problem
Original language description
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in its explicit formulation and then convert it into an equivalent MDP, that can be solved with deep reinforcement learning. Also, we evaluate a real-world inspired setting with sparse training datasets with missing features. The presented method performs robustly well in all settings across several distinct datasets, outperforming other prior-art algorithms. The method is flexible, as showcased with all mentioned modifications and can be improved with any domain independent advancement in RL.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Learning
ISSN
0885-6125
e-ISSN
1573-0565
Volume of the periodical
109
Issue of the periodical within the volume
8
Country of publishing house
US - UNITED STATES
Number of pages
29
Pages from-to
1587-1615
UT code for WoS article
000517008900001
EID of the result in the Scopus database
2-s2.0-85081286072