基于模糊神经网络和深度神经网络的混合人格预测框架

Nazila Taghvaei, B. Masoumi, M. Keyvanpour
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引用次数: 3

摘要

一般来说,人类是非常复杂的生物体,因此,研究他们的各个维度和方面,包括个性,已经成为一个有吸引力的研究课题。随着技术的出现,社交网络背景下的一种新的交流方式的出现,也给人类带来了一种新的社会交流形式,在这个新的空间中对人的识别和分类已经成为研究的热点,受到了许多研究者的挑战。本文针对个体的大五人格特征,首先提出了相关工作的分类,然后提出了基于模糊神经网络(FNN)和深度神经网络(DNN)的混合框架,通过将不同的FNN分类器与DNN分类器相结合,提出了两阶段决策融合方案,提高了人格识别的准确性。最后,对该方法进行了仿真。提出的方法是使用社会网络分析(SNA)的结构特征,以及从个人活动描述中提取的语言分析(LA)特征,并与先前的类似研究进行比较。结果很好地说明了所提出的框架在myPersonality数据集上的性能改进,达到平均准确率的83.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks
In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of individuals, first, categorization of related work is proposed, and then a hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural Networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The proposed approach is using the structural features of Social Networks Analysis (SNA), along with a linguistic analysis (LA) feature extracted from the description of the activities of individuals and comparison with the previous similar researches. The results, well-illustrated the performance improvement of the proposed framework up to 83.2 % of average accuracy on myPersonality dataset.
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