Face Recognition with Explanation by Fuzzy Rules and Linguistic Description

Rutkowska, Danuta; Kurach, Damian; Rakus-Andersson, Elisabeth

doi:10.1007/978-3-030-61401-0_32

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Various general approaches to the aggregation of classifiers were already presented (e.g., in publications of Alsina et al, 2006 ; Beliakov et al, 2007 ; Grabisch et al, 2009 ; Calvo et al, 2012 ; Gągolewski, 2015 ; Dolecki et al, 2016 ; Baczyński et al, 2017 ). Recently, one of the dominant techniques is using the Choquet integral or its generalizations or extensions ( Kwak and Pedrycz, 2004 , 2005 ; Karczmarek et al, 2014 , 2017a , b , 2018 , 2019b ; Anderson et al, 2018 ; Rutkowska et al, 2020 ). In particular, recent studies on the so-called pre-aggregation functions offer hope for the development of this approach ( Lucca et al, 2015 , 2016 , 2017 ; Bustince et al, 2016 ; Dimuro et al, 2017 ; Dias et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals

Plechawska–Wójcik

Karczmarek

Krukow

et al. 2021

Front. Neuroinform.

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In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.

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