Classification with Costly Features Using Deep Reinforcement Learning

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification with Costly Features Using Deep Reinforcement Learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Classification with Costly Features Using Deep Reinforcement Learning

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-809-1

ISSN

2159-5399

e-ISSN

—

Počet stran výsledku

8

Strana od-do

3959-3966

Název nakladatele

AAAI Press

Místo vydání

Menlo Park, California

Místo konání akce

Honolulu

Datum konání akce

27. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000485292603120