Effective Automatic Method Selection for Nonlinear Regression Modeling

Result code in 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>

Alternative codes found

RIV/00216208:11320/21:10434504

Result on the web

<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>

Result language

angličtina

Original language name

Effective Automatic Method Selection for Nonlinear Regression Modeling

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

1793-6462

Volume of the periodical

31

Issue of the periodical within the volume

10

Country of publishing house

SG - SINGAPORE

Number of pages

12

Pages from-to

2150020

UT code for WoS article

000696596800003

EID of the result in the Scopus database

2-s2.0-85104028019