Data Quality Assessment for ML Decision-Making

Data quality has a strong effect on the design, validation and testing of decision-making systems. New paradigms of future models in the knowledge society need to analyze clean, complete, consistent, and high-quality data. This paper presents three case studies from different fields in which models are constructed using machine learning strategies.

Projects on text recognition, electrocardiogram-based identification and data analysis are described in relation to input data quality and system performance.