基于随机森林分类器和粒子群优化的社交媒体6种人格特征预测

Iksan Ramadhan
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摘要

在决定一个人是否能胜任一项工作,或者哪个职位适合他的性格时,性格评估的重要性是必需的。通常使用的人格模型是迈尔斯布里格斯类型指标,但该模型被认为不适合使用,因为该模型在定义一个人的人格时过于二分。与HEXACO人格模型不同,该模型提供了对每个人格特征的数值评估。在确定一个人的个性时,可以通过使用分类算法模型利用一个人的写作来完成。在测试中,将之前收集到的数据按照其目标进行分类,以人格分类的形式得到结果。使用PSO算法进行测试,使获得的结果具有高水平的准确性,等于0.9(90%)。利用粒子群算法在预测过程中自动搜索预测模型中的参数值,可以获得较高的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of 6 Personality Characters (HEXACO) from Social Media using Random Forest Classifier and Particle Swarm Optimization
The importance of personality assessment is required in determining whether an individual can support a job, or which position is suitable and in accordance with his personality. The personality model that is often used is the Myer Brigs Type Indicator, but the model is considered inappropriate for use because the model is too dichotomous in defining one's personality. Unlike the HEXACO personality model, the model provides a numerical assessment of each personality trait. In determining an individual's personality, it can be done by utilizing one's writing using the classification algorithm model. In the test, the data collected previously was classified according to its target to get the results in the form of personality classifications. Testing is done using the PSO algorithm so that the results obtained have a high level of accuracy, equal to 0.9 (90%). The use of PSO algorithm can help to get high accuracy results in the prediction process because the search for parameter values in the prediction model is done automatically.
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