Classification with Costly Features Using Deep Reinforcement Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332656" target="_blank" >RIV/68407700:21230/19:00332656 - isvavai.cz</a>
Result on the web
<a href="https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4287/4165" target="_blank" >https://wvvw.aaai.org/ojs/index.php/AAAI/article/view/4287/4165</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1609/aaai.v33i01.33013959" target="_blank" >10.1609/aaai.v33i01.33013959</a>
Alternative languages
Result language
angličtina
Original language name
Classification with Costly Features Using Deep Reinforcement Learning
Original language description
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision. On a set of eight problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem. The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifier, and unlike prior art, its performance is robust across all evaluated datasets.
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
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
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
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
ISBN
978-1-57735-809-1
ISSN
2159-5399
e-ISSN
—
Number of pages
8
Pages from-to
3959-3966
Publisher name
AAAI Press
Place of publication
Menlo Park, California
Event location
Honolulu
Event date
Jan 27, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000485292603120