Multi-Objective Evolutionary Design of Explainable EEG Classifier

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Multi-Objective Evolutionary Design of Explainable EEG Classifier

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

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

<a href="/en/project/GA24-10990S" target="_blank" >GA24-10990S: Hardware-Aware Machine Learning: From Automated Design to Innovative and Explainable Solutions</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Article name in the collection

Genetic Programming, 28th European Conference, EuroGP 2025

ISBN

978-3-031-89990-4

ISSN

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e-ISSN

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Number of pages

16

Pages from-to

52-67

Publisher name

Springer Nature Switzerland AG

Place of publication

Terst

Event location

Terst

Event date

Apr 23, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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