Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00373937" target="_blank" >RIV/68407700:21230/24:00373937 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-49252-5_5" target="_blank" >https://doi.org/10.1007/978-3-031-49252-5_5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-49252-5_5" target="_blank" >10.1007/978-3-031-49252-5_5</a>

Result language

angličtina

Original language name

Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework

Original language description

Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random; one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and then compared with each other. The results show that the FIUL-Data framework allows the evaluation of the resilience of ML models. In most experiments cases, ML models show higher resilience at larger training datasets, where gradient boost performed better than support vector regression in smaller training sets. Overall, the mean squared error metric is useful in evaluating the resilience of models due to its higher sensitivity to data mutation.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

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

2024

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-031-49251-8

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

18

Pages from-to

42-59

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

—

Event location

Västerås

Event date

Oct 16, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—