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Conditional generative positive and unlabeled learning
ID
Papič, Aleš
(
Avtor
),
ID
Kononenko, Igor
(
Avtor
),
ID
Bosnić, Zoran
(
Avtor
)
PDF - Predstavitvena datoteka,
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(2,35 MB)
MD5: AE62BE3CBF81988AF54F09C2764422A5
URL - Izvorni URL, za dostop obiščite
https://www.sciencedirect.com/science/article/pii/S0957417423005481
Galerija slik
Izvleček
The quantity of data generated increases daily, which makes it difficult to process. In the case of supervised learning, labeling training examples may represent an especially tedious and costly task. One of the aims of positive and unlabeled (PU) learning is to train a binary classifier from partially labeled data, representing a strategy for combining supervised and semi-supervised learning and alleviating the cost of labeling data fully. Still, the main strength of PU learning arises when the negative data are not directly available or too diverse. Although the generative approaches have shown promising results in this field, they also bring shortcomings, such as high computational cost, training instability, and inability to generate fully labeled datasets. In the paper, we propose a novel Conditional Generative PU framework (CGenPU) with a built-in auxiliary classifier. We develop a novel loss function to learn the distribution of positive and negative examples, which leads to a unique, desirable equilibrium under a nonparametric assumption. Our CGenPU is evaluated against existing generative approaches using both synthetic and real data. The characteristics of various methods, including ours, are depicted with different toy examples. The results demonstrate the state-of-the-art performance on standard positive and unlabeled learning benchmark datasets. Given only ten labeled CIFAR-10 examples, CGenPU achieves classification accuracy of 84%, while current state-of-the-art D-GAN framework achieves 54%. On top of that, CGenPU is the first single-stage generative framework for PU learning.
Jezik:
Angleški jezik
Ključne besede:
positive and unlabeled learning
,
partially supervised learning
,
generative adversarial networks
,
deep learning
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FRI - Fakulteta za računalništvo in informatiko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2023
Št. strani:
13 str.
Številčenje:
Vol. 224, art. 120046
PID:
20.500.12556/RUL-146102
UDK:
004.8
ISSN pri članku:
0957-4174
DOI:
10.1016/j.eswa.2023.120046
COBISS.SI-ID:
148488195
Datum objave v RUL:
19.05.2023
Število ogledov:
439
Število prenosov:
63
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Objavi na:
Gradivo je del revije
Naslov:
Expert systems with applications
Skrajšan naslov:
Expert syst. appl.
Založnik:
Elsevier
ISSN:
0957-4174
COBISS.SI-ID:
171291
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
učenje iz pozitivnih in neoznačenih primerov
,
delno nadzorovano učenje
,
generativne nasprotniške mreže
,
globoko učenje
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Program financ.:
Young researchers
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P2-0209
Naslov:
Umetna inteligenca in inteligentni sistemi
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