Abstract
Traditional statistical analysis is oriented towards finding linear relationships between the variables under investigation, often accompanied by strict assumptions about the problem and data distributions. Moreover, traditional analysis endorses data reduction as much as possible before modeling, and, as a result, part of the original information is lost. On the other hand, machine learning does not impose rigid pre-assumptions about the problem and data distributions since the underlying ratio is to “learn from data”, without the need for data reduction or a priori knowledge before the learning.
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Muzzioli, S. (2019). The Future of Fuzzy Sets in Finance: New Challenges in Machine Learning and Explainable AI. In: Fullér, R., Giove, S., Masulli, F. (eds) Fuzzy Logic and Applications. WILF 2018. Lecture Notes in Computer Science(), vol 11291. Springer, Cham. https://doi.org/10.1007/978-3-030-12544-8_26
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