| Naslov: | NiaAML : AutoML for classification and regression pipelines |
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| Avtorji: | ID Fister, Iztok (Avtor)
ID Farthofer, Laurenz A. (Avtor)
ID Pečnik, Luka (Avtor)
ID Fister, Iztok (Avtor)
ID Holzinger, Andreas (Avtor) |
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| Datoteke: |  1-s2.0-S2352711024003443-main.pdf (1,43 MB)
MD5: FBCA90EC58A3352C978C54E1254CC2BB
 https://www.sciencedirect.com/science/article/pii/S2352711024003443?via%3Dihub#da1
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
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| Opis: | In this paper we present NiaAML, an AutoML framework that we have developed for creating machine learning pipelines and hyperparameter tuning. The composition of machine learning pipelines is presented as an optimization problem that can be solved using various stochastic, population-based, nature-inspired algorithms. Nature-inspired algorithms are powerful tools for solving real-world optimization problems, especially those that are highly complex, nonlinear, and involve large search spaces where traditional algorithms may struggle. They are applied widely in various fields, including robotics, operations research, and bioinformatics. This paper provides a comprehensive overview of the software architecture, and describes the main tasks of NiaAML, including the automatic composition of classification and regression pipelines. The overview is supported by an practical illustrative example. |
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| Ključne besede: | AutoML, classification, nature-inspired algorithms, optimization |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 15.08.2024 |
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| Datum sprejetja članka: | 08.11.2024 |
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| Datum objave: | 20.11.2024 |
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| Založnik: | Elsevier |
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| Leto izida: | 2025 |
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| Št. strani: | 5 str. |
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| Številčenje: | Vol. 29, št. članka 101974 |
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| PID: | 20.500.12556/DKUM-96557  |
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| UDK: | 004.8 |
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| COBISS.SI-ID: | 219923971 |
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| DOI: | 10.1016/j.softx.2024.101974 |
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| ISSN pri članku: | 2352-7110 |
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| Avtorske pravice: | © 2024 The Authors |
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| Datum objave v DKUM: | 19.01.2026 |
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| Število ogledov: | 180 |
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| Število prenosov: | 14 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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