Guest Editorial Special Section on Fuzzy Systems in Data Science

The papers in this special section address the use of fuzzy systems in data science. Fuzzy Systems in Data Science data science employs theories and techniques drawn from many fields to achieve knowledge extraction from large volumes of data. To facilitate and automate the achievement of useful insights, predictions, and decisions from collected data sets, scientists and engineers turned to Machine Learning solutions. Currently, with the birth of Big Data, the set of opportunities for inquiry has grown exponentially, thanks to these large and complex data sets. Being able to exploit this massive data effectively provides useful knowledge for decision-making or the exploration and comprehension of the phenomenon that produced the data. Data science includes several problems and tasks depending on the nature of the data and the type of knowledge that is to be extracted. From predictive and descriptive analysis to the application of recommendation systems, scientists and practitioners have considered the use of Soft Computing techniques based on fuzzy sets to overcome their problems. Fuzzy approaches have been applied with much success in several areas related to Data Science. Fuzzy sets can be used to effectively describe and incorporate uncertain data values, data measurement, and data relations; fuzzy systems can directly aid reasoning and inferencing in a learning machine and can be combined with machine learning methodologies to model human behaviors and complex data systems.