Effective Automatic Method Selection for Nonlinear Regression Modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00541777" target="_blank" >RIV/67985807:_____/21:00541777 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/21:10434504

Výsledek na webu

<a href="http://dx.doi.org/10.1142/S0129065721500209" target="_blank" >http://dx.doi.org/10.1142/S0129065721500209</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0129065721500209" target="_blank" >10.1142/S0129065721500209</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Effective Automatic Method Selection for Nonlinear Regression Modeling

Popis výsledku v původním jazyce

Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regression modeling, particularly in recommending either the standard nonlinear least squares estimator or one of such available alternative estimators, which is highly robust with respect to the presence of outliers in the data. The authors hold the opinion that theoretical considerations will never be able to formulate such recommendations for the nonlinear regression context. Instead, metalearning is explored here as an original approach suitable for this task. In this paper, four different approaches for automatic method selection for nonlinear regression are proposed and computations over a training database of 643 real publicly available datasets are performed. Particularly, while the metalearning results may be harmed by the imbalanced number of groups, an effective approach yields much improved results, performing a novel combination of supervised feature selection by random forest and oversampling by synthetic minority oversampling technique (SMOTE). As a by-product, the computations bring arguments in favor of the very recent nonlinear least weighted squares estimator, which turns out to outperform other (and much more renowned) estimators in a quite large percentage of datasets.

Název v anglickém jazyce

Effective Automatic Method Selection for Nonlinear Regression Modeling

Popis výsledku anglicky

Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regression modeling, particularly in recommending either the standard nonlinear least squares estimator or one of such available alternative estimators, which is highly robust with respect to the presence of outliers in the data. The authors hold the opinion that theoretical considerations will never be able to formulate such recommendations for the nonlinear regression context. Instead, metalearning is explored here as an original approach suitable for this task. In this paper, four different approaches for automatic method selection for nonlinear regression are proposed and computations over a training database of 643 real publicly available datasets are performed. Particularly, while the metalearning results may be harmed by the imbalanced number of groups, an effective approach yields much improved results, performing a novel combination of supervised feature selection by random forest and oversampling by synthetic minority oversampling technique (SMOTE). As a by-product, the computations bring arguments in favor of the very recent nonlinear least weighted squares estimator, which turns out to outperform other (and much more renowned) estimators in a quite large percentage of datasets.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

1793-6462

Svazek periodika

31

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

12

Strana od-do

2150020

Kód UT WoS článku

000696596800003

EID výsledku v databázi Scopus

2-s2.0-85104028019