Interpreting support vector machines applied in laser-induced breakdown spectroscopy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU142522" target="_blank" >RIV/00216305:26620/22:PU142522 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/22:00126745

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0003267021011788#appsec1" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0003267021011788#appsec1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.aca.2021.339352" target="_blank" >10.1016/j.aca.2021.339352</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interpreting support vector machines applied in laser-induced breakdown spectroscopy

Popis výsledku v původním jazyce

Laser-induced breakdown spectroscopy is often combined with a multivariate black box model—such as support vector machines (SVMs)—to obtain desirable quantitative or qualitative results. This approach carries obvious risks when practiced in high-stakes applications. Moreover, the lack of understanding of a black-box model limits the user's ability to fine-tune the model. Thus, here we present four approaches to interpret SVMs through investigating which features the models consider important in the classification task of 19 algal and cyanobacterial species. The four feature importance metrics are compared with popular approaches to feature selection for optimal SVM performance. We report that the distinct feature importance metrics yield complementary and often comparable information. In addition, we identify our SVM model's bias towards features with a large variance, even though these features exhibit a significant overlap between classes. We also show that the linear and radial basis kernel SVMs weight the same features to the same degree.

Název v anglickém jazyce

Interpreting support vector machines applied in laser-induced breakdown spectroscopy

Popis výsledku anglicky

Laser-induced breakdown spectroscopy is often combined with a multivariate black box model—such as support vector machines (SVMs)—to obtain desirable quantitative or qualitative results. This approach carries obvious risks when practiced in high-stakes applications. Moreover, the lack of understanding of a black-box model limits the user's ability to fine-tune the model. Thus, here we present four approaches to interpret SVMs through investigating which features the models consider important in the classification task of 19 algal and cyanobacterial species. The four feature importance metrics are compared with popular approaches to feature selection for optimal SVM performance. We report that the distinct feature importance metrics yield complementary and often comparable information. In addition, we identify our SVM model's bias towards features with a large variance, even though these features exhibit a significant overlap between classes. We also show that the linear and radial basis kernel SVMs weight the same features to the same degree.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10406 - Analytical chemistry

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Analytica Chimica Acta

ISSN

0003-2670

e-ISSN

1873-4324

Svazek periodika

1192

Číslo periodika v rámci svazku

339352

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000829967000016

EID výsledku v databázi Scopus

2-s2.0-85121215317