Title: Revolutionizing Data Labeling: Overcoming Challenges and Embracing Automated Approaches
In today’s data-centric world, supervised learning stands as the cornerstone of machine learning. However, the success of this approach heavily relies on labeled data. Unfortunately, vast amounts of industry data remain unlabeled, making data labeling a crucial step in the data preparation process. With the need for domain-specific knowledge and the time-consuming nature of labeling, companies are now in search of more efficient, accurate, and cost-effective approaches to tackle this challenge. In this article, we unveil the challenges faced by the industry, explore strategies for mitigation, and shed light on the best approaches to data labeling.
To gain a comprehensive understanding of the field, a multidisciplinary research effort was undertaken. This research encompassed a systematic mapping study to identify the main labeling approaches and their respective application domains. Additionally, a case study involving two companies was conducted, involving an internship with one company and interviews with data scientists from both organizations. The data collected from these sources was then subjected to thematic analysis to formulate the challenges encountered and their corresponding mitigation strategies.
The findings of this research shed light on several key areas. Firstly, we provide an overview of popular data labeling approaches used in various applications, accompanied by an examination of the datasets used to evaluate their effectiveness. Furthermore, we categorize and define the industry challenges faced by data scientists. In response to these challenges, we present effective mitigation strategies. Additionally, we identify the most accurate automated labeling approaches and evaluate the level of manual effort required to achieve optimal accuracy. Finally, we unveil the best benchmark datasets for evaluating automatic labeling approaches.
Looking ahead, future work in the field can focus on the examination of safe and deep semi-supervised learning and its practical applications. The rise of semi-supervised learning based on Deep Learning in recent years necessitates a deeper exploration of its potential.
By revolutionizing data labeling approaches, companies can make significant strides in optimizing their machine learning models. The challenges identified in this research provide valuable insights into the industry’s pain points, while the strategies highlighted offer practical solutions. As the world continues to generate vast amounts of data, it becomes imperative to embrace automated techniques that ease the labeling process. With greater accuracy and efficiency, companies can unlock the true potential of their data and advance in this data-driven era.
Together, industry professionals and researchers can pave the way for a future where data labeling becomes a seamless and optimized process. By leveraging the latest technologies, exploring new methodologies, and embracing collaborative efforts, we can revolutionize data labeling and empower organizations in every sector.