ArticleEntropy (Basel, Switzerland)2021
Why Do Big Data and Machine Learning Entail the Fractional Dynamics?
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A survey on fractal fractional nonlinear Kawahara equation theoretical and computational analysis.Scientific reports · 2024Article
- Combining Fractional Derivatives and Machine Learning: A Review.Entropy (Basel, Switzerland) · 2022Review
- The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks.Frontiers in network physiology · 2022Article
- Fractional Calculus and the Future of Science.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: "is there a more optimal way to optimize?". Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.
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