Using Machine Learning to Aid in Data Classification: Classifying Occupation Compatibility with Highly Automated Vehicles

A. Kamaraj, John D. Lee
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引用次数: 2

Abstract

Data classification is central to human factors research, and manual data classification is tedious and error prone. Supervised learning enables analysts to train an algorithm by manually classifying a few cases and then have that algorithm classify many cases. However, algorithms often fail to leverage human insight. To address this, we augment supervised learning with unsupervised learning and data visualization. Unsupervised learning highlights potential classification errors, explains the underlying classification, and identifies additional cases that merit manual classification. We illustrate this using the Occupational Information Network database to classify occupations as having tasks that might be performed in an automated vehicle.
使用机器学习辅助数据分类:对高度自动化车辆的职业兼容性进行分类
数据分类是人为因素研究的核心,手工数据分类繁琐且容易出错。监督学习使分析人员能够通过手动对少数案例进行分类来训练算法,然后让该算法对许多案例进行分类。然而,算法往往无法利用人类的洞察力。为了解决这个问题,我们用无监督学习和数据可视化来增强监督学习。无监督学习强调潜在的分类错误,解释潜在的分类,并确定值得手动分类的其他情况。我们使用职业信息网络数据库来说明这一点,将职业分类为具有可能在自动车辆中执行的任务。
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