Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity

被引：5

作者：

Mamontov, Danila ^{[1
]}

Polonskaia, Iana ^{[1
]}

Skorokhod, Alina ^{[1
]}

Semenkin, Eugene ^{[1
]}

Kessler, Viktor ^{[2
]}

Schwenker, Friedhelm ^{[2
]}

机构：

[1] Reshetnev Siberian State Univ Sci & Technol, 31 Krasnoyarskiy Rabochiy Prospect, Krasnoyarsk 660014, Russia

[2] Ulm Univ, Inst Neural Informat Proc, D-89081 Ulm, Germany

来源：

MULTIMODAL PATTERN RECOGNITION OF SOCIAL SIGNALS IN HUMAN-COMPUTER-INTERACTION, MPRSS 2018 | 2019年 / 11377卷

关键词：

Multimodal pain intensity recognition; Evolutionary algorithm; Neural network;

D O I：

10.1007/978-3-030-20984-1_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a study on multi-modal pain intensity recognition based on video and bio-physiological sensor data. The newly recorded SenseEmotion dataset consisting of 40 individuals, each subjected to three gradually increasing levels of painful heat stimuli, has been used for the evaluation of the proposed algorithms. We propose and evaluated evolutionary algorithms for the design and adaptation of the structure of deep artificial neural network architectures. Feedforward Neural Network and Recurrent Neural Network have been considered for the optimisation by using a Self-Configuring Genetic Algorithm (Self-CGA) and Self-Configuring Genetic Programming (SelfCGP).

引用

页码：84 / 100

页数：17

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