Data Collection and Labeling Techniques for Machine Learning

This remarkable advancement can be attributed to two key factors: the exponential rise in computational power and the ever-increasing availability of vast datasets [1-3]. However, the very foundation upon which this progress rests-data collection and labeling-presents significant challenges that can hinder the efficacy and ethical implementation of ML models[4-8]. This review paper delves into the intricate world of data collection and labeling for machine learning, drawing upon insights from both the data management and machine learning communities. The transformative potential of machine learning is evident across a multitude of domains. From revolutionizing healthcare with disease diagnosis and personalized medicine[9] to powering selfdriving cars[10] and optimizing logistics in supply chains[11], ML algorithms are rapidly reshaping our world. At the heart of these advancements lies the ability of ML models to learn from data, identify patterns, and make predictions based on the information they have been exposed to. The quality and quantity of data used to train these models are paramount to their success. High-quality, diverse, and well-labeled data are essential for building robust and generalizable ML models that can perform effectively in real-world scenarios [12, 13].