Classification with Costly Features as a Sequential Decision-making Problem

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification with Costly Features as a Sequential Decision-making Problem

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Classification with Costly Features as a Sequential Decision-making Problem

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2020

Kód důvěrnosti údajů

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

Název periodika

Machine Learning

ISSN

0885-6125

e-ISSN

1573-0565

Svazek periodika

109

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

29

Strana od-do

1587-1615

Kód UT WoS článku

000517008900001

EID výsledku v databázi Scopus

2-s2.0-85081286072