Toward an Optimal and Structured Feature Subset Selection for Multi-Target Regression Using Genetic Algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253304" target="_blank" >RIV/61989100:27240/23:10253304 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10296894" target="_blank" >https://ieeexplore.ieee.org/document/10296894</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3327870" target="_blank" >10.1109/ACCESS.2023.3327870</a>

Result language

angličtina

Original language name

Toward an Optimal and Structured Feature Subset Selection for Multi-Target Regression Using Genetic Algorithm

Original language description

Multi Target Regression (MTR) is a machine learning method that simultaneously predicts multiple real-valued outputs using a set of input variables. A lot of emerging applications that can be mapped to this class of problem. In MTR method one of the critical aspect is to handle structural information like instance and target correlation. MTR algorithms attempt to exploit these interdependences when building a model. This results in increased model complexities, which in turn, reduce the interpretability of the model through manual analysis of the result. However, data driven real-world applications often require models that can be used to analyze and improve real-world workflows. Leveraging dimensionality reduction techniques can reduce model complexity while retaining the performance and boost interpretability. This research proposes multiple feature subset alternatives for MTR using genetic algorithm, and provides a comparison of the different feature subset selection alternatives in conjunction with MTR algorithms. We proposed a genetic algorithm based feature subset selection with all targets and with individual target keeping the structural information intact in the selection process. Experiments are performed on real world benchmarked MTR data sets and the results indicate that a significant improvement in performance can be obtained with comparatively simple MTR models by utilizing optimal and structured feature selection. (C) 2013 IEEE.

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

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

2023

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

121966-121977

UT code for WoS article

001102109200001

EID of the result in the Scopus database

2-s2.0-85176721975