A Pitfall of Learning from User-generated Data: In-depth Analysis of Subjective Class Problem

Research in the supervised learning algorithms field implicitly assumes that training data is labeled by domain experts or at least semi-professional labelers accessible through crowdsourcing services like Amazon Mechanical Turk. With the advent of the Internet, data has become abundant and a large number of machine learning based systems started being trained with user-generated data, using categorical data as true labels. However, little work has been done in the area of supervised learning with user-defined labels where users are not necessarily experts and might be motivated to provide incorrect labels in order to improve their own utility from the system. In this article, we propose two types of classes in user-defined labels: subjective class and objective class - showing that the objective classes are as reliable as if they were provided by domain experts, whereas the subjective classes are subject to bias and manipulation by the user. We define this as a subjective class issue and provide a framework for detecting subjective labels in a dataset without querying oracle. Using this framework, data mining practitioners can detect a subjective class at an early stage of their projects, and avoid wasting their precious time and resources by dealing with subjective class problem with traditional machine learning techniques.