一种基于模糊模型的新型职业预测方法-模糊集群方法
1School of Education Science, Nanjing Normal University, Nanjing 210097, China; Jiangsu Vocational Institute of Commerce, Nanjing 211168, China.
Acta psychologica
|August 28, 2025
概括
这项研究引入了一种新的职业预测模糊模型, 与现有方法相比,该模型的准确性更高,改善了职业选择和招聘结果.
科学领域:
- 心理学
- 计算机科学
- 职业发展
背景情况:
- 有效的职业决策对于工作场所的竞争力和最佳招聘至关重要.
- 职业预测工具有助于个人了解职业,评估风险并提高信心.
- 现有的职业预测方法面临不确定性和复杂数据的挑战.
研究的目的:
- 开发和评估一个用于提高职业预测的模糊模型.
- 在职业决策中利用模糊逻辑处理不确定性的能力.
- 提高与适合职业的个人匹配的准确性和效率.
主要方法:
- 应用了包含职业兴趣和个性特征的模糊模型 (RIASEC代码).
- 使用模糊集群来管理多个输入变量并简化模糊规则的确定.
- 将模糊模型的预测精度与机器学习方法进行比较.
主要成果:
- 这种模糊模型的预测准确度高于传统的概况和机器学习方法.
- 模糊聚类有效地解决了"模糊规则爆炸"问题.
- 该模型成功地将个人的职业兴趣和个性特征与合适的职业进行了映射.
结论:
- 拟议的模糊模型为职业预测提供了更准确,更科学的方法.
- 模糊模型为处理职业选择中固有的不确定性提供了一个强大的框架.
- 这种方法可以帮助个人做出明智的职业决策,也可以帮助雇主招聘员工.
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