Evaluation of neural network feature and function settings on the model performance and accuracy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14510%2F23%3A00130991" target="_blank" >RIV/00216224:14510/23:00130991 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.efsupit.ro/images/stories/aprilie2023/Art%20123.pdf" target="_blank" >https://www.efsupit.ro/images/stories/aprilie2023/Art%20123.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7752/jpes.2023.04123" target="_blank" >10.7752/jpes.2023.04123</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of neural network feature and function settings on the model performance and accuracy

Popis výsledku v původním jazyce

Both in general and in sport, the neural network is one of the most frequently used type of the artificial intelligence algorithms. Due to their high price, wealthy sports clubs could only afford to use it on a daily basis in the past. The situation has changed with the development of affordable softwares and their manuals. Although sports club managers can employ them more nowadays, there is still a problem how to prepare the data ideally and to set up the most efficient model algorithm. In the executed studies and literature sources, the function setting has been examined and emphasised more, whereas the set-up of the feature setting has been rather neglected. It has only been recommended to improve the model, but there have not yet been sufficient observation. The current study aims to determine if the features or the function settings have a greater effect on the model accuracy. The initial feature dataset (n = 18882) was obtained from publicly available sources. Each of the six different feature settings consisted of 96 models. A total of 384 models were created, in which their testing accuracy and the percentage difference between the training and the testing phases were further analyzed. No statistically significant differences were found in the accuracy of the function settings, but statistically significant differences were found in the feature settings. Based on the results, the current study concludes that the feature settings are a more important factor to increase the model accuracy than the function settings, especially the reduction of the number of the outputs. Furthermore, the study found that the variables Weight, Height, and Age had the highest frequency of the occurrence of the normalized importance. Therefore, they can be identified as among the most important features to predict the final rank. The results of this study suggest that more emphasis should be put on the feature setting and not just on the function setting when preparing the first model of artificial intelligence.

Název v anglickém jazyce

Evaluation of neural network feature and function settings on the model performance and accuracy

Popis výsledku anglicky

Both in general and in sport, the neural network is one of the most frequently used type of the artificial intelligence algorithms. Due to their high price, wealthy sports clubs could only afford to use it on a daily basis in the past. The situation has changed with the development of affordable softwares and their manuals. Although sports club managers can employ them more nowadays, there is still a problem how to prepare the data ideally and to set up the most efficient model algorithm. In the executed studies and literature sources, the function setting has been examined and emphasised more, whereas the set-up of the feature setting has been rather neglected. It has only been recommended to improve the model, but there have not yet been sufficient observation. The current study aims to determine if the features or the function settings have a greater effect on the model accuracy. The initial feature dataset (n = 18882) was obtained from publicly available sources. Each of the six different feature settings consisted of 96 models. A total of 384 models were created, in which their testing accuracy and the percentage difference between the training and the testing phases were further analyzed. No statistically significant differences were found in the accuracy of the function settings, but statistically significant differences were found in the feature settings. Based on the results, the current study concludes that the feature settings are a more important factor to increase the model accuracy than the function settings, especially the reduction of the number of the outputs. Furthermore, the study found that the variables Weight, Height, and Age had the highest frequency of the occurrence of the normalized importance. Therefore, they can be identified as among the most important features to predict the final rank. The results of this study suggest that more emphasis should be put on the feature setting and not just on the function setting when preparing the first model of artificial intelligence.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of Physical Education and Sport (JPES)

ISSN

2247-8051

e-ISSN

2247-806X

Svazek periodika

23

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

RO - Rumunsko

Počet stran výsledku

7

Strana od-do

983-989

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85161034387