Название: Nature Inspired Computing for Data Science Автор: Minakhi Rout, Jitendra Kumar Rout
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2020
Страниц: 302
Язык: английский
Формат: pdf (true)
Размер: 10.8 MB
This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.
Machine Learning (ML) and data mining tasks in Big Data involve different nature of inputs that typically exhibit high dimensionality, e.g. more than 1,000 features, far from current acceptable scales computing in one machine. In many different domains, data have highly nonlinear representations that nature-inspired models can easily capture, outperforming simple models. But, the usage of these approaches in high-dimensional data are computationally costly. Recently, artificial hydrocarbon networks (AHN) - a supervised learning method inspired on organic chemical structures and mechanisms - have shown improvements in predictive power and interpretability in contrast with other well-known machine learning models, such as neural networks and random forests. However, AHN are very time-consuming that are not able to deal with big data until now. In this chapter, we present a fast and reliable nature-inspired training method for AHN, so they can handle high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined both individual encoding and objective function related to the AHN-model, and it is also implemented in parallel-computing.
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