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학술대회 The Design of Fuzzy C-Means Clustering Based Neural Networks for Emotion Classification
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저자
박병준, 장은혜, 김상협, 허철, 정명애
발행일
201306
출처
Joint International Fuzzy Systems Association World Congress and North American Fuzzy Information Processing Society Annual Meeting (IFSA/NAFIPS) 2013, pp.413-417
DOI
https://dx.doi.org/10.1109/IFSA-NAFIPS.2013.6608436
협약과제
12SF1300, 시각 생체 모방 소자 및 인지 시스템 기술 개발, 정명애
초록
In this study, we investigate the use of a Fuzzy C-Means Clustering based Neural Network (FNN) classifier in problems of emotion classification. The proposed classifier model consists of three layers, namely, input, hidden and output layers. Here, fuzzy c-means clustering method, two types of polynomial and linear combination function are used as a kernel function in the input layer, the hidden layer and the output layer of networks, respectively. From the conceptual standpoint, the classifier of this form is constructed by employing two development mechanisms. Fuzzy clustering (Fuzzy C-Means, FCM) is aimed at the development of input layer of the networks while the corresponding neurons of the networks are formed by some local polynomials. The purpose of this study is to classify the emotions using physiological signals induced by three different emotions (boredom, pain and surprise). Three different emotional states are evoked by emotional stimuli, physiological signals (EDA, ECG, PPG and SKT) for the induced emotions are measured as the reactions of stimuli, and 27 features are extracted from their physiological signals for emotion classification using the proposed FNN classifier. To evaluate the performance of emotion classification of the proposed model, we use the 10-fold cross validation and a comparative analysis shows that the proposed model exhibit higher accuracy when compared with some other models that exist in the literature. © 2013 IEEE.
KSP 제안 키워드
Classifier model, Clustering-Based, Comparative analysis, Cross validation(CV), Emotion classification, Emotional states, Fuzzy Clustering, Fuzzy c-means clustering method, Hidden layer, Input layer, Kernel function