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Overcoming "Big Data" Barriers in Machine Learning Techniques for the Real-Life Applications

Data analysis, regardless of whether the data are expected to explain a quantitative (as in regression) or categorical (as in classification) models, often requires overcoming various barriers. They include unbalanced datasets, faulty measurement results, and incomplete data. A special group of barriers is typical for what nowadays is referred to as “Big Data.” The term is used to characterize problems where the available datasets are too large to easily deal with traditional machine learning tools and approaches. It is now generally accepted that dealing with huge and complex data sets poses many processing challenges and opens a range of research and technological problems and calls for new approaches. Big Data is often characterized by the well-known 5V properties: