Multi-Objective Evolutionary Design of Explainable EEG Classifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0193309" target="_blank" >RIV/00216305:26230/26:0193309 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-89991-1_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-89991-1_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-89991-1_4" target="_blank" >10.1007/978-3-031-89991-1_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Objective Evolutionary Design of Explainable EEG Classifier

Popis výsledku v původním jazyce

Deep neural networks (DNNs) have achieved impressive results in many fields. However, the use of black-box solutions based on DNNs in medical applications poses challenges, as understanding the rationale behind decisions is crucial for application in healthcare. For those reasons, we propose a new method for the evolutionary multi-objective design (MOD) of small and potentially explainable EEG (Electroencephalography) signal classifiers. We evaluate a combination of genetic algorithm (GA) for feature selection with multiple algorithms for the automated design of the classifier, including Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. To further improve the classification quality and obtain less complex solutions, we compare three different MOD scenarios targeting the accuracy, specificity, sensitivity, and the number of used features. In addition, we evaluate the use of Cartesian Genetic Programming (CGP) as a way to achieve smaller and more interpretable solutions and combine it with the compositional co-evolution of selected features to improve computational requirements and find solutions in a reasonable time. The proposed methods are experimentally evaluated on tasks of alcohol use disorder and major depressive disorder classification. Experimental results show that newly proposed MOD scenarios lead to significantly better trade-offs between the accuracy and the number of features compared to the state-of-the-art method employing the NSGA-II algorithm. The proposed co-evolution of features (evolved by GA) and classifier (evolved by CGP) allowed the design of small and potentially explainable solutions and led to 20-100 times faster convergence than the baseline CGP-based approach.

Název v anglickém jazyce

Multi-Objective Evolutionary Design of Explainable EEG Classifier

Popis výsledku anglicky

Deep neural networks (DNNs) have achieved impressive results in many fields. However, the use of black-box solutions based on DNNs in medical applications poses challenges, as understanding the rationale behind decisions is crucial for application in healthcare. For those reasons, we propose a new method for the evolutionary multi-objective design (MOD) of small and potentially explainable EEG (Electroencephalography) signal classifiers. We evaluate a combination of genetic algorithm (GA) for feature selection with multiple algorithms for the automated design of the classifier, including Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. To further improve the classification quality and obtain less complex solutions, we compare three different MOD scenarios targeting the accuracy, specificity, sensitivity, and the number of used features. In addition, we evaluate the use of Cartesian Genetic Programming (CGP) as a way to achieve smaller and more interpretable solutions and combine it with the compositional co-evolution of selected features to improve computational requirements and find solutions in a reasonable time. The proposed methods are experimentally evaluated on tasks of alcohol use disorder and major depressive disorder classification. Experimental results show that newly proposed MOD scenarios lead to significantly better trade-offs between the accuracy and the number of features compared to the state-of-the-art method employing the NSGA-II algorithm. The proposed co-evolution of features (evolved by GA) and classifier (evolved by CGP) allowed the design of small and potentially explainable solutions and led to 20-100 times faster convergence than the baseline CGP-based approach.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA24-10990S" target="_blank" >GA24-10990S: Strojové učení zohledňující hardware: Od automatizovaného návrhu k inovativním a vysvětlitelným řešením</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Genetic Programming, 28th European Conference, EuroGP 2025

ISBN

978-3-031-89990-4

ISSN

—

e-ISSN

—

Počet stran výsledku

16

Strana od-do

52-67

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Terst

Místo konání akce

Terst

Datum konání akce

23. 4. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—