中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Research and Modeling of Cognitive Rule of Name Novelty Based on Machine Learning and Random Forest Method

文献类型:会议论文

作者Wang, Chang1,2; Ren, Xiaopeng1,2; Wang, Yihan1,2
出版日期2023
会议名称2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering, ECICE 2023
会议日期2023
会议地点不详
通讯作者邮箱ren, xiaopeng
DOI10.1109/ECICE59523.2023.10383122
页码736-740
英文摘要

Different names make people experience differences in novelty. Names make people feel novel, while others make people feel ordinary. There is a certain pattern in people's perception of the novelty of names. We conducted a survey questionnaire on the novelty of names based on 100 young parents and explored the novelty of 200 real names among young parents as the dependent variable for the study. Then, based on the relevant theories of name novelty research, feature extraction was performed on these 200 names as independent variables. Based on the decision tree method and the random forest method, the relationship between name novelty and name features was studied. The experimental results showed that the F1-score of the random forest model reached 85.4%, which better fitted the cognitive patterns of young parents towards name novelty but the interpretability of the random forest model is poor. The F1-score of the decision tree model reached 81.6%, which also was a high accuracy, and the interpretability of the decision tree model was strong. The decision tree model showed the 'frequency of use in Chinese characters', 'frequency of use in names', 'length of names', 'number of strokes', and 'number of results on the search engine' as key variables that affect the novelty of names.

收录类别EI
产权排序1
会议录2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering
语种英语
源URL[http://ir.psych.ac.cn/handle/311026/46878]  
专题中国科学院心理研究所
作者单位1.University of Chinese Academy of Sciences, Department of Psychology, Beijing, China
2.Institute of Psychology, Chinese Academy of Sciences, Cas Key Laboratory of Behavioral Sciences, Beijing, China
推荐引用方式
GB/T 7714
Wang, Chang,Ren, Xiaopeng,Wang, Yihan. Research and Modeling of Cognitive Rule of Name Novelty Based on Machine Learning and Random Forest Method[C]. 见:2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering, ECICE 2023. 不详. 2023.

入库方式: OAI收割

来源:心理研究所

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