A Domain-region Based Evaluation of ML Performance Robustness to Covariate Shift

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00370203" target="_blank" >RIV/68407700:21230/23:00370203 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s00521-023-08622-w" target="_blank" >https://doi.org/10.1007/s00521-023-08622-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-023-08622-w" target="_blank" >10.1007/s00521-023-08622-w</a>

Result language

angličtina

Original language name

A Domain-region Based Evaluation of ML Performance Robustness to Covariate Shift

Original language description

Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpected performance of the learned model in deployment. The issue in which the training and test data inputs follow different probability distributions while the input-output relationship remains unchanged is referred to as covariate shift. In this paper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariate shift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function of the input data to assess the classifier's performance per domain region. Distributional changes were simulated in a two-dimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on the experimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing the lowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the results reveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that the models exhibit high bias toward the region with high density in the input space domain of the training samples.

Czech name

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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

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OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Volume of the periodical

35

Issue of the periodical within the volume

24

Country of publishing house

GB - UNITED KINGDOM

Number of pages

23

Pages from-to

17555-17577

UT code for WoS article

000988193400003

EID of the result in the Scopus database

2-s2.0-85159332578